Bindings for mrpt::slam namespace

Classes
		mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t(pybind11_builtins.pybind11_object) CRangeBearingKFSLAM2D mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t(pybind11_builtins.pybind11_object) CRangeBearingKFSLAM mrpt.pymrpt.mrpt.bayes.CRejectionSamplingCapable_mrpt_poses_CPose2D_mrpt_bayes_particle_storage_mode_POINTER_t(pybind11_builtins.pybind11_object) CRejectionSamplingRangeOnlyLocalization mrpt.pymrpt.mrpt.config.CLoadableOptions(pybind11_builtins.pybind11_object) TKLDParams mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles(mrpt.pymrpt.mrpt.poses.CPose3DPDF, mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, mrpt.pymrpt.mrpt.Stringifyable) CMonteCarloLocalization3D(mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) mrpt.pymrpt.mrpt.poses.CPosePDFParticles(mrpt.pymrpt.mrpt.poses.CPosePDF, mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, mrpt.pymrpt.mrpt.Stringifyable) CMonteCarloLocalization2D(mrpt.pymrpt.mrpt.poses.CPosePDFParticles, PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) mrpt.pymrpt.mrpt.serialization.CSerializable(mrpt.pymrpt.mrpt.rtti.CObject) CIncrementalMapPartitioner mrpt.pymrpt.mrpt.system.CObserver(pybind11_builtins.pybind11_object) COccupancyGridMapFeatureExtractor pybind11_builtins.pybind11_object(builtins.object) CMetricMapBuilder CMetricMapBuilderICP CMetricMapBuilderRBPF CMetricMapsAlignmentAlgorithm CGridMapAligner CICP PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t CMonteCarloLocalization2D(mrpt.pymrpt.mrpt.poses.CPosePDFParticles, PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t CMonteCarloLocalization3D(mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) TDataAssociationMethod TDataAssociationMetric TDataAssociationResults TICPAlgorithm TICPCovarianceMethod TMetricMapAlignmentResult TMonteCarloLocalizationParams map_keyframe_t similarity_method_t   class CGridMapAligner(CMetricMapsAlignmentAlgorithm)     A class for aligning two multi-metric maps (with an occupancy grid maps and a points map, at least) based on features extraction and matching. The matching pose is returned as a Sum of Gaussians (poses::CPosePDFSOG).    This class can use three methods (see options.methodSelection):   - amCorrelation: "Brute-force" correlation of the two maps over a 2D+orientation grid of possible 2D poses.   - amRobustMatch: Detection of features + RANSAC matching   - amModifiedRANSAC: Detection of features + modified multi-hypothesis RANSAC matching as described in was reported in the paper https://www.mrpt.org/Paper%3AOccupancy_Grid_Matching   See CGridMapAligner::Align for more instructions.     CMetricMapsAlignmentAlgorithm     Method resolution order: CGridMapAligner CMetricMapsAlignmentAlgorithm pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CGridMapAligner) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CGridMapAligner, arg0: mrpt.pymrpt.mrpt.slam.CGridMapAligner) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CGridMapAligner, arg0: mrpt.pymrpt.mrpt.slam.CGridMapAligner) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CGridMapAligner, : mrpt.pymrpt.mrpt.slam.CGridMapAligner) -> mrpt.pymrpt.mrpt.slam.CGridMapAligner   C++: mrpt::slam::CGridMapAligner::operator=(const class mrpt::slam::CGridMapAligner &) --> class mrpt::slam::CGridMapAligner & Data descriptors defined here: options Data and other attributes defined here: TAlignerMethod = <class 'mrpt.pymrpt.mrpt.slam.CGridMapAligner.TAlignerMethod'> TConfigParams = <class 'mrpt.pymrpt.mrpt.slam.CGridMapAligner.TConfigParams'> The ICP algorithm configuration data TReturnInfo = <class 'mrpt.pymrpt.mrpt.slam.CGridMapAligner.TReturnInfo'> The ICP algorithm return information. amCorrelation = <TAlignerMethod.amCorrelation: 1> amModifiedRANSAC = <TAlignerMethod.amModifiedRANSAC: 2> amRobustMatch = <TAlignerMethod.amRobustMatch: 0> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CICP(CMetricMapsAlignmentAlgorithm)     Several implementations of ICP (Iterative closest point) algorithms for aligning two point maps or a point map wrt a grid map.    CICP::AlignPDF() or CICP::Align() are the two main entry points of the algorithm.    To choose among existing ICP algorithms or customizing their parameters, see CICP::TConfigParams and the member     There exists an extension of the original ICP algorithm that provides multihypotheses-support for the correspondences, and which generates a Sum-of-Gaussians (SOG)    PDF as output. See mrpt::tfest::se2_l2_robust()   For further details on the implemented methods, check the web:   https://www.mrpt.org/Iterative_Closest_Point_(ICP)_and_other_matching_algorithms    For a paper explaining some of the basic equations, see for example:   J. Martinez, J. Gonzalez, J. Morales, A. Mandow, A. Garcia-Cerezo,   "Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms",    Journal of Field Robotics, vol. 23, no. 1, pp. 21-34, 2006. ( http://babel.isa.uma.es/~jlblanco/papers/martinez2006gil.pdf )     CMetricMapsAlignmentAlgorithm     Method resolution order: CICP CMetricMapsAlignmentAlgorithm pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CICP) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CICP, icpParams: mrpt::slam::CICP::TConfigParams) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CICP, arg0: mrpt.pymrpt.mrpt.slam.CICP) -> None   4. __init__(self: mrpt.pymrpt.mrpt.slam.CICP, arg0: mrpt.pymrpt.mrpt.slam.CICP) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CICP, : mrpt.pymrpt.mrpt.slam.CICP) -> mrpt.pymrpt.mrpt.slam.CICP   C++: mrpt::slam::CICP::operator=(const class mrpt::slam::CICP &) --> class mrpt::slam::CICP & Data descriptors defined here: options Data and other attributes defined here: TConfigParams = <class 'mrpt.pymrpt.mrpt.slam.CICP.TConfigParams'> The ICP algorithm configuration data TReturnInfo = <class 'mrpt.pymrpt.mrpt.slam.CICP.TReturnInfo'> The ICP algorithm return information Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CIncrementalMapPartitioner(mrpt.pymrpt.mrpt.serialization.CSerializable)     Finds partitions in metric maps based on N-cut graph partition theory.     Method resolution order: CIncrementalMapPartitioner mrpt.pymrpt.mrpt.serialization.CSerializable mrpt.pymrpt.mrpt.rtti.CObject pybind11_builtins.pybind11_object builtins.object Methods defined here: GetRuntimeClass(...) GetRuntimeClass(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::slam::CIncrementalMapPartitioner::GetRuntimeClass() const --> const struct mrpt::rtti::TRuntimeClassId * __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, arg0: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, arg0: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> None addMapFrame(...) addMapFrame(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, frame: mrpt.pymrpt.mrpt.obs.CSensoryFrame, robotPose3D: mrpt.pymrpt.mrpt.poses.CPose3DPDF) -> int   Insert a new keyframe to the graph.    Call this method each time a new observation is added to the map/graph.  Afterwards, call updatePartitions() to get the updated partitions.      The sensed data      An estimation of the robot global pose.      The index of the new pose in the graph, which can be used to  refer to this pose in the future.      updatePartitions   C++: mrpt::slam::CIncrementalMapPartitioner::addMapFrame(const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3DPDF &) --> uint32_t assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, : mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner   C++: mrpt::slam::CIncrementalMapPartitioner::operator=(const class mrpt::slam::CIncrementalMapPartitioner &) --> class mrpt::slam::CIncrementalMapPartitioner & changeCoordinatesOrigin(...) changeCoordinatesOrigin(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, newOrigin: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   Change the coordinate origin of all stored poses    Used for consistency with future new poses to enter in the system.   C++: mrpt::slam::CIncrementalMapPartitioner::changeCoordinatesOrigin(const class mrpt::poses::CPose3D &) --> void changeCoordinatesOriginPoseIndex(...) changeCoordinatesOriginPoseIndex(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, newOriginPose: int) -> None   The new origin is given by the index of the pose that is to  become the new origin.   C++: mrpt::slam::CIncrementalMapPartitioner::changeCoordinatesOriginPoseIndex(unsigned int) --> void clear(...) clear(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> None   C++: mrpt::slam::CIncrementalMapPartitioner::clear() --> void clone(...) clone(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::slam::CIncrementalMapPartitioner::clone() const --> class mrpt::rtti::CObject * getAdjacencyMatrix(...) getAdjacencyMatrix(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> mrpt.pymrpt.mrpt.math.CMatrixDynamic_double_t   Return a const ref to the internal adjacency matrix.     C++: mrpt::slam::CIncrementalMapPartitioner::getAdjacencyMatrix() const --> const class mrpt::math::CMatrixDynamic<double> & getNodesCount(...) getNodesCount(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> int   Get the total node count currently in the internal map/graph.    C++: mrpt::slam::CIncrementalMapPartitioner::getNodesCount() --> size_t getSequenceOfFrames(...) getSequenceOfFrames(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner) -> mrpt.pymrpt.mrpt.maps.CSimpleMap   Access to the sequence of Sensory Frames    C++: mrpt::slam::CIncrementalMapPartitioner::getSequenceOfFrames() --> class mrpt::maps::CSimpleMap * setSimilarityMethod(...) setSimilarityMethod(*args, **kwargs) Overloaded function.   1. setSimilarityMethod(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, method: mrpt.pymrpt.mrpt.slam.similarity_method_t) -> None   Select the similarity method to use for newly inserted keyframes    C++: mrpt::slam::CIncrementalMapPartitioner::setSimilarityMethod(enum mrpt::slam::similarity_method_t) --> void   2. setSimilarityMethod(self: mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner, func: std::function<double (mrpt::slam::map_keyframe_t const&, mrpt::slam::map_keyframe_t const&, mrpt::poses::CPose3D const&)>) -> None   Sets a custom function for the similarity of new keyframes    C++: mrpt::slam::CIncrementalMapPartitioner::setSimilarityMethod(class std::function<double (const struct mrpt::slam::map_keyframe_t &, const struct mrpt::slam::map_keyframe_t &, const class mrpt::poses::CPose3D &)>) --> void Static methods defined here: CreateObject(...) from builtins.PyCapsule CreateObject() -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::slam::CIncrementalMapPartitioner::CreateObject() --> class std::shared_ptr<class mrpt::rtti::CObject> GetRuntimeClassIdStatic(...) from builtins.PyCapsule GetRuntimeClassIdStatic() -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::slam::CIncrementalMapPartitioner::GetRuntimeClassIdStatic() --> const struct mrpt::rtti::TRuntimeClassId & Data descriptors defined here: options Data and other attributes defined here: TOptions = <class 'mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner.TOptions'> Configuration parameters Methods inherited from mrpt.pymrpt.mrpt.rtti.CObject: duplicateGetSmartPtr(...) duplicateGetSmartPtr(self: mrpt.pymrpt.mrpt.rtti.CObject) -> mrpt.pymrpt.mrpt.rtti.CObject   Makes a deep copy of the object and returns a smart pointer to it    C++: mrpt::rtti::CObject::duplicateGetSmartPtr() const --> class std::shared_ptr<class mrpt::rtti::CObject> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMetricMapBuilder(pybind11_builtins.pybind11_object)     This virtual class is the base for SLAM implementations. See derived classes for more information.     CMetricMap     Method resolution order: CMetricMapBuilder pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> None clear(...) clear(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> None   Clear all elements of the maps, and reset localization to (0,0,0deg).    C++: mrpt::slam::CMetricMapBuilder::clear() --> void enableMapUpdating(...) enableMapUpdating(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, enable: bool) -> None   Enables or disables the map updating (default state is enabled)    C++: mrpt::slam::CMetricMapBuilder::enableMapUpdating(bool) --> void getCurrentPoseEstimation(...) getCurrentPoseEstimation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> mrpt.pymrpt.mrpt.poses.CPose3DPDF   Returns a copy of the current best pose estimation as a pose PDF.    C++: mrpt::slam::CMetricMapBuilder::getCurrentPoseEstimation() const --> class std::shared_ptr<class mrpt::poses::CPose3DPDF> getCurrentlyBuiltMap(...) getCurrentlyBuiltMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, out_map: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   Fills "out_map" with the set of "poses"-"sensory-frames", thus the so  far built map.    C++: mrpt::slam::CMetricMapBuilder::getCurrentlyBuiltMap(class mrpt::maps::CSimpleMap &) const --> void getCurrentlyBuiltMapSize(...) getCurrentlyBuiltMapSize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> int   Returns just how many sensory-frames are stored in the currently build  map.    C++: mrpt::slam::CMetricMapBuilder::getCurrentlyBuiltMapSize() --> unsigned int getCurrentlyBuiltMetricMap(...) getCurrentlyBuiltMetricMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> mrpt.pymrpt.mrpt.maps.CMultiMetricMap   Returns the map built so far. NOTE that for efficiency a pointer to the  internal object is passed, DO NOT delete nor modify the object in any  way, if desired, make a copy of ir with "clone()".    C++: mrpt::slam::CMetricMapBuilder::getCurrentlyBuiltMetricMap() const --> const class mrpt::maps::CMultiMetricMap & initialize(...) initialize(*args, **kwargs) Overloaded function.   1. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> None   2. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   3. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap, x0: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   Initialize the method, starting with a known location PDF "x0"(if  supplied, set to nullptr to left unmodified) and a given fixed, past map.   C++: mrpt::slam::CMetricMapBuilder::initialize(const class mrpt::maps::CSimpleMap &, const class mrpt::poses::CPosePDF *) --> void loadCurrentMapFromFile(...) loadCurrentMapFromFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   Load map (mrpt::maps::CSimpleMap) from a ".simplemap" file    C++: mrpt::slam::CMetricMapBuilder::loadCurrentMapFromFile(const std::string &) --> void processActionObservation(...) processActionObservation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observations: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> None   Process a new action and observations pair to update this map: See the description of the class at the top of this page to see a more complete description.       The estimation of the incremental pose change in the robot pose.             The set of observations that robot senses at the new pose.   C++: mrpt::slam::CMetricMapBuilder::processActionObservation(class mrpt::obs::CActionCollection &, class mrpt::obs::CSensoryFrame &) --> void saveCurrentEstimationToImage(...) saveCurrentEstimationToImage(*args, **kwargs) Overloaded function.   1. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, file: str) -> None   2. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, file: str, formatEMF_BMP: bool) -> None   A useful method for debugging: the current map (and/or poses) estimation  is dumped to an image file.      The output file name      Output format = true:EMF, false:BMP   C++: mrpt::slam::CMetricMapBuilder::saveCurrentEstimationToImage(const std::string &, bool) --> void saveCurrentMapToFile(...) saveCurrentMapToFile(*args, **kwargs) Overloaded function.   1. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   2. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str, compressGZ: bool) -> None   Save map (mrpt::maps::CSimpleMap) to a ".simplemap" file.    C++: mrpt::slam::CMetricMapBuilder::saveCurrentMapToFile(const std::string &, bool) const --> void Data and other attributes defined here: TOptions = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilder.TOptions'> Options for the algorithm Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMetricMapBuilderICP(CMetricMapBuilder)     A class for very simple 2D SLAM based on ICP. This is a non-probabilistic pose tracking algorithm.    Map are stored as in files as binary dumps of "mrpt::maps::CSimpleMap" objects. The methods are          thread-safe.     Method resolution order: CMetricMapBuilderICP CMetricMapBuilder pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP) -> None getCurrentPoseEstimation(...) getCurrentPoseEstimation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP) -> mrpt.pymrpt.mrpt.poses.CPose3DPDF   Returns a copy of the current best pose estimation as a pose PDF.   C++: mrpt::slam::CMetricMapBuilderICP::getCurrentPoseEstimation() const --> class std::shared_ptr<class mrpt::poses::CPose3DPDF> getCurrentlyBuiltMap(...) getCurrentlyBuiltMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, out_map: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   Fills "out_map" with the set of "poses"-"sensory-frames", thus the so  far built map    C++: mrpt::slam::CMetricMapBuilderICP::getCurrentlyBuiltMap(class mrpt::maps::CSimpleMap &) const --> void getCurrentlyBuiltMapSize(...) getCurrentlyBuiltMapSize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP) -> int   Returns just how many sensory-frames are stored in the currently build  map    C++: mrpt::slam::CMetricMapBuilderICP::getCurrentlyBuiltMapSize() --> unsigned int getCurrentlyBuiltMetricMap(...) getCurrentlyBuiltMetricMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP) -> mrpt.pymrpt.mrpt.maps.CMultiMetricMap   C++: mrpt::slam::CMetricMapBuilderICP::getCurrentlyBuiltMetricMap() const --> const class mrpt::maps::CMultiMetricMap & initialize(...) initialize(*args, **kwargs) Overloaded function.   1. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP) -> None   2. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   3. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap, x0: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   Initialize the method, starting with a known location PDF "x0"(if  supplied, set to nullptr to left unmodified) and a given fixed, past map.   This method MUST be called if using the default constructor, after  loading the configuration into ICP_options. In particular,  TConfigParams::mapInitializers   C++: mrpt::slam::CMetricMapBuilderICP::initialize(const class mrpt::maps::CSimpleMap &, const class mrpt::poses::CPosePDF *) --> void processActionObservation(...) processActionObservation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, action: mrpt.pymrpt.mrpt.obs.CActionCollection, in_SF: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> None   Appends a new action and observations to update this map: See the description of the class at the top of this page to see a more complete description.       The estimation of the incremental pose change in the robot pose.             The set of observations that robot senses at the new pose.  See params in CMetricMapBuilder::options and CMetricMapBuilderICP::ICP_options      processObservation   C++: mrpt::slam::CMetricMapBuilderICP::processActionObservation(class mrpt::obs::CActionCollection &, class mrpt::obs::CSensoryFrame &) --> void processObservation(...) processObservation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, obs: mrpt.pymrpt.mrpt.obs.CObservation) -> None   The main method of this class: Process one odometry or sensor          observation.                 The new entry point of the algorithm (the old one  was          processActionObservation, which now is a wrapper to           this method).  See params in CMetricMapBuilder::options and          CMetricMapBuilderICP::ICP_options   C++: mrpt::slam::CMetricMapBuilderICP::processObservation(const class std::shared_ptr<class mrpt::obs::CObservation> &) --> void saveCurrentEstimationToImage(...) saveCurrentEstimationToImage(*args, **kwargs) Overloaded function.   1. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, file: str) -> None   2. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, file: str, formatEMF_BMP: bool) -> None   A useful method for debugging: the current map (and/or poses) estimation  is dumped to an image file.      The output file name      Output format = true:EMF, false:BMP   C++: mrpt::slam::CMetricMapBuilderICP::saveCurrentEstimationToImage(const std::string &, bool) --> void setCurrentMapFile(...) setCurrentMapFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP, mapFile: str) -> None   Sets the "current map file", thus that map will be loaded if it exists  or a new one will be created if it does not, and the updated map will be  save to that file when destroying the object.   C++: mrpt::slam::CMetricMapBuilderICP::setCurrentMapFile(const char *) --> void Data descriptors defined here: ICP_options ICP_params Data and other attributes defined here: TConfigParams = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilderICP.TConfigParams'> Algorithm configuration params Methods inherited from CMetricMapBuilder: clear(...) clear(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder) -> None   Clear all elements of the maps, and reset localization to (0,0,0deg).    C++: mrpt::slam::CMetricMapBuilder::clear() --> void enableMapUpdating(...) enableMapUpdating(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, enable: bool) -> None   Enables or disables the map updating (default state is enabled)    C++: mrpt::slam::CMetricMapBuilder::enableMapUpdating(bool) --> void loadCurrentMapFromFile(...) loadCurrentMapFromFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   Load map (mrpt::maps::CSimpleMap) from a ".simplemap" file    C++: mrpt::slam::CMetricMapBuilder::loadCurrentMapFromFile(const std::string &) --> void saveCurrentMapToFile(...) saveCurrentMapToFile(*args, **kwargs) Overloaded function.   1. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   2. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str, compressGZ: bool) -> None   Save map (mrpt::maps::CSimpleMap) to a ".simplemap" file.    C++: mrpt::slam::CMetricMapBuilder::saveCurrentMapToFile(const std::string &, bool) const --> void Data and other attributes inherited from CMetricMapBuilder: TOptions = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilder.TOptions'> Options for the algorithm Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMetricMapBuilderRBPF(CMetricMapBuilder)     This class implements a Rao-Blackwelized Particle Filter (RBPF) approach to  map building (SLAM).    Internally, the list of particles, each containing a hypothesis for the  robot path plus its associated     metric map, is stored in an object of class CMultiMetricMapPDF.     This class processes robot actions and observations sequentially (through  the method CMetricMapBuilderRBPF::processActionObservation)    and exploits the generic design of metric map classes in MRPT to deal with  any number and combination of maps simultaneously: the likelihood    of observations is the product of the likelihood in the different maps,  etc.      A number of particle filter methods are implemented as well, by selecting  the appropriate values in TConstructionOptions::PF_options.    Not all the PF algorithms are implemented for all kinds of maps.     For an example of usage, check the application "rbpf-slam", in  "apps/RBPF-SLAM". See also the  * href="http://www.mrpt.org/Application:RBPF-SLAM" >wiki page.       Since MRPT 0.7.2, the new variables  "localizeLinDistance,localizeAngDistance" are introduced to provide a way to  update the robot pose at a different rate than the map is updated.       Since MRPT 0.7.1 the semantics of the parameters  "insertionLinDistance" and "insertionAngDistance" changes: the entire RBFP is  now NOT updated unless odometry increments surpass the threshold (previously,  only the map was NOT updated). This is done to gain efficiency.       Since MRPT 0.6.2 this class implements full 6D SLAM. Previous versions  worked in 2D + heading only.      CMetricMap     Method resolution order: CMetricMapBuilderRBPF CMetricMapBuilder pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, initializationOptions: mrpt::slam::CMetricMapBuilderRBPF::TConstructionOptions) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, src: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF   Copy Operator.    C++: mrpt::slam::CMetricMapBuilderRBPF::operator=(const class mrpt::slam::CMetricMapBuilderRBPF &) --> class mrpt::slam::CMetricMapBuilderRBPF & clear(...) clear(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> None   Clear all elements of the maps.   C++: mrpt::slam::CMetricMapBuilderRBPF::clear() --> void drawCurrentEstimationToImage(...) drawCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, img: mrpt.pymrpt.mrpt.img.CCanvas) -> None   A useful method for debugging: draws the current map and path hypotheses  to a CCanvas     C++: mrpt::slam::CMetricMapBuilderRBPF::drawCurrentEstimationToImage(class mrpt::img::CCanvas *) --> void getCurrentJointEntropy(...) getCurrentJointEntropy(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> float   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentJointEntropy() --> double getCurrentMostLikelyPath(...) getCurrentMostLikelyPath(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, outPath: List[mrpt.pymrpt.mrpt.math.TPose3D]) -> None   Returns the current most-likely path estimation (the path associated to  the most likely particle).   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentMostLikelyPath(class std::deque<struct mrpt::math::TPose3D> &) const --> void getCurrentPoseEstimation(...) getCurrentPoseEstimation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> mrpt.pymrpt.mrpt.poses.CPose3DPDF   Returns a copy of the current best pose estimation as a pose PDF.   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentPoseEstimation() const --> class std::shared_ptr<class mrpt::poses::CPose3DPDF> getCurrentlyBuiltMap(...) getCurrentlyBuiltMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, out_map: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   Fills "out_map" with the set of "poses"-"sensory-frames", thus the so  far built map.   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentlyBuiltMap(class mrpt::maps::CSimpleMap &) const --> void getCurrentlyBuiltMapSize(...) getCurrentlyBuiltMapSize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> int   Returns just how many sensory-frames are stored in the currently build  map.   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentlyBuiltMapSize() --> unsigned int getCurrentlyBuiltMetricMap(...) getCurrentlyBuiltMetricMap(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> mrpt.pymrpt.mrpt.maps.CMultiMetricMap   Returns the map built so far. NOTE that for efficiency a pointer to the  internal object is passed, DO NOT delete nor modify the object in any  way, if desired, make a copy of ir with "clone()".   C++: mrpt::slam::CMetricMapBuilderRBPF::getCurrentlyBuiltMetricMap() const --> const class mrpt::maps::CMultiMetricMap & initialize(...) initialize(*args, **kwargs) Overloaded function.   1. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF) -> None   2. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap) -> None   3. initialize(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, initialMap: mrpt.pymrpt.mrpt.maps.CSimpleMap, x0: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   Initialize the method, starting with a known location PDF "x0"(if  supplied, set to nullptr to left unmodified) and a given fixed, past map.   C++: mrpt::slam::CMetricMapBuilderRBPF::initialize(const class mrpt::maps::CSimpleMap &, const class mrpt::poses::CPosePDF *) --> void processActionObservation(...) processActionObservation(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observations: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> None   Appends a new action and observations to update this map: See the description of the class at the top of this page to see a more complete description.       The incremental 2D pose change in the robot pose. This value is deterministic.             The set of observations that robot senses at the new pose.   Statistics will be saved to statsLastIteration   C++: mrpt::slam::CMetricMapBuilderRBPF::processActionObservation(class mrpt::obs::CActionCollection &, class mrpt::obs::CSensoryFrame &) --> void saveCurrentEstimationToImage(...) saveCurrentEstimationToImage(*args, **kwargs) Overloaded function.   1. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, file: str) -> None   2. saveCurrentEstimationToImage(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, file: str, formatEMF_BMP: bool) -> None   A useful method for debugging: the current map (and/or poses) estimation  is dumped to an image file.      The output file name      Output format = true:EMF, false:BMP   C++: mrpt::slam::CMetricMapBuilderRBPF::saveCurrentEstimationToImage(const std::string &, bool) --> void saveCurrentPathEstimationToTextFile(...) saveCurrentPathEstimationToTextFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF, fil: str) -> None   A logging utility: saves the current path estimation for each particle  in a text file (a row per particle, each 3-column-entry is a set  [x,y,phi], respectively).   C++: mrpt::slam::CMetricMapBuilderRBPF::saveCurrentPathEstimationToTextFile(const std::string &) --> void Data descriptors defined here: m_statsLastIteration mapPDF Data and other attributes defined here: TConstructionOptions = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF.TConstructionOptions'> Options for building a CMetricMapBuilderRBPF object, passed to the constructor. TStats = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilderRBPF.TStats'> This structure will hold stats after each execution of processActionObservation Methods inherited from CMetricMapBuilder: enableMapUpdating(...) enableMapUpdating(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, enable: bool) -> None   Enables or disables the map updating (default state is enabled)    C++: mrpt::slam::CMetricMapBuilder::enableMapUpdating(bool) --> void loadCurrentMapFromFile(...) loadCurrentMapFromFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   Load map (mrpt::maps::CSimpleMap) from a ".simplemap" file    C++: mrpt::slam::CMetricMapBuilder::loadCurrentMapFromFile(const std::string &) --> void saveCurrentMapToFile(...) saveCurrentMapToFile(*args, **kwargs) Overloaded function.   1. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str) -> None   2. saveCurrentMapToFile(self: mrpt.pymrpt.mrpt.slam.CMetricMapBuilder, fileName: str, compressGZ: bool) -> None   Save map (mrpt::maps::CSimpleMap) to a ".simplemap" file.    C++: mrpt::slam::CMetricMapBuilder::saveCurrentMapToFile(const std::string &, bool) const --> void Data and other attributes inherited from CMetricMapBuilder: TOptions = <class 'mrpt.pymrpt.mrpt.slam.CMetricMapBuilder.TOptions'> Options for the algorithm Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMetricMapsAlignmentAlgorithm(pybind11_builtins.pybind11_object)     A base class for any algorithm able of maps alignment. There are two methods   depending on an PDF or a single 2D Pose value is available as initial guess for the methods.     CPointsMap     Method resolution order: CMetricMapsAlignmentAlgorithm pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(self, /, *args, **kwargs) Initialize self.  See help(type(self)) for accurate signature. assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CMetricMapsAlignmentAlgorithm, : mrpt.pymrpt.mrpt.slam.CMetricMapsAlignmentAlgorithm) -> mrpt.pymrpt.mrpt.slam.CMetricMapsAlignmentAlgorithm   C++: mrpt::slam::CMetricMapsAlignmentAlgorithm::operator=(const class mrpt::slam::CMetricMapsAlignmentAlgorithm &) --> class mrpt::slam::CMetricMapsAlignmentAlgorithm & Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMonteCarloLocalization2D(mrpt.pymrpt.mrpt.poses.CPosePDFParticles, PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t)     Declares a class that represents a Probability Density Function (PDF) over a 2D pose (x,y,phi), using a set of weighted samples.    This class also implements particle filtering for robot localization. See the MRPT   application "app/pf-localization" for an example of usage.     CMonteCarloLocalization3D, CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable     Method resolution order: CMonteCarloLocalization2D mrpt.pymrpt.mrpt.poses.CPosePDFParticles mrpt.pymrpt.mrpt.poses.CPosePDF mrpt.pymrpt.mrpt.serialization.CSerializable mrpt.pymrpt.mrpt.rtti.CObject mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable mrpt.pymrpt.mrpt.Stringifyable PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t pybind11_builtins.pybind11_object builtins.object Methods defined here: PF_SLAM_computeObservationLikelihoodForParticle(...) PF_SLAM_computeObservationLikelihoodForParticle(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, particleIndexForMap: int, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, x: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Evaluate the observation likelihood for one particle at a given location   C++: mrpt::slam::CMonteCarloLocalization2D::PF_SLAM_computeObservationLikelihoodForParticle(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D &) const --> double PF_SLAM_implementation_custom_update_particle_with_new_pose(...) PF_SLAM_implementation_custom_update_particle_with_new_pose(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, particleData: mrpt.pymrpt.mrpt.math.TPose2D, newPose: mrpt.pymrpt.mrpt.math.TPose3D) -> None   C++: mrpt::slam::CMonteCarloLocalization2D::PF_SLAM_implementation_custom_update_particle_with_new_pose(struct mrpt::math::TPose2D *, const struct mrpt::math::TPose3D &) const --> void __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D) -> None   doc   2. __init__(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, M: int) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, : mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D) -> mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D   C++: mrpt::slam::CMonteCarloLocalization2D::operator=(const class mrpt::slam::CMonteCarloLocalization2D &) --> class mrpt::slam::CMonteCarloLocalization2D & getLastPose(...) getLastPose(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, i: int, is_valid_pose: bool) -> mrpt.pymrpt.mrpt.math.TPose3D                   @{     Return the robot pose for the i'th particle. is_valid is  always true in this class.    C++: mrpt::slam::CMonteCarloLocalization2D::getLastPose(size_t, bool &) const --> struct mrpt::math::TPose3D getVisualization(...) getVisualization(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D) -> mrpt.pymrpt.mrpt.opengl.CSetOfObjects   Returns a 3D representation of this PDF.      Needs the mrpt-opengl library, and using  mrpt::opengl::CSetOfObjects::Ptr as template argument.   C++: mrpt::slam::CMonteCarloLocalization2D::getVisualization() const --> class std::shared_ptr<class mrpt::opengl::CSetOfObjects> prediction_and_update_pfAuxiliaryPFOptimal(...) prediction_and_update_pfAuxiliaryPFOptimal(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfAuxiliaryPFOptimal(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void prediction_and_update_pfAuxiliaryPFStandard(...) prediction_and_update_pfAuxiliaryPFStandard(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfAuxiliaryPFStandard(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void prediction_and_update_pfStandardProposal(...) prediction_and_update_pfStandardProposal(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfStandardProposal(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void resetUniformFreeSpace(...) resetUniformFreeSpace(*args, **kwargs) Overloaded function.   1. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D) -> None   2. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float) -> None   3. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int) -> None   4. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float) -> None   5. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float, x_max: float) -> None   6. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float, x_max: float, y_min: float) -> None   7. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float, x_max: float, y_min: float, y_max: float) -> None   8. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float, x_max: float, y_min: float, y_max: float, phi_min: float) -> None   9. resetUniformFreeSpace(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization2D, theMap: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, freeCellsThreshold: float, particlesCount: int, x_min: float, x_max: float, y_min: float, y_max: float, phi_min: float, phi_max: float) -> None   Reset the PDF to an uniformly distributed one, but only in the  free-space    of a given 2D occupancy-grid-map. Orientation is randomly generated in  the whole 2*PI range.      The occupancy grid map      The minimum free-probability to consider a  cell as empty (default is 0.7)      If set to -1 the number of m_particles remains  unchanged.      The limits of the area to look for free cells.      The limits of the area to look for free cells.      The limits of the area to look for free cells.      The limits of the area to look for free cells.      The limits of the area to look for free cells.      The limits of the area to look for free cells.       resetDeterm32inistic      std::exception On any error (no free cell found in map,  map=nullptr, etc...)   C++: mrpt::slam::CMonteCarloLocalization2D::resetUniformFreeSpace(class mrpt::maps::COccupancyGridMap2D *, const double, const int, const double, const double, const double, const double, const double, const double) --> void Data descriptors defined here: options Methods inherited from mrpt.pymrpt.mrpt.poses.CPosePDFParticles: GetRuntimeClass(...) GetRuntimeClass(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::poses::CPosePDFParticles::GetRuntimeClass() const --> const struct mrpt::rtti::TRuntimeClassId * __iadd__(...) __iadd__(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, Ap: mrpt.pymrpt.mrpt.math.TPose2D) -> None   Appends (pose-composition) a given pose "p" to each particle   C++: mrpt::poses::CPosePDFParticles::operator+=(const struct mrpt::math::TPose2D &) --> void append(...) append(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, o: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> None   Appends (add to the list) a set of m_particles to the existing ones, and  then normalize weights.   C++: mrpt::poses::CPosePDFParticles::append(class mrpt::poses::CPosePDFParticles &) --> void asString(...) asString(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> str   C++: mrpt::poses::CPosePDFParticles::asString() const --> std::string bayesianFusion(...) bayesianFusion(*args, **kwargs) Overloaded function.   1. bayesianFusion(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, p1: mrpt.pymrpt.mrpt.poses.CPosePDF, p2: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   2. bayesianFusion(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, p1: mrpt.pymrpt.mrpt.poses.CPosePDF, p2: mrpt.pymrpt.mrpt.poses.CPosePDF, minMahalanobisDistToDrop: float) -> None   Bayesian fusion.   C++: mrpt::poses::CPosePDFParticles::bayesianFusion(const class mrpt::poses::CPosePDF &, const class mrpt::poses::CPosePDF &, const double) --> void changeCoordinatesReference(...) changeCoordinatesReference(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, newReferenceBase: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   this = p (+) this. This can be used to convert a PDF from local  coordinates to global, providing the point (newReferenceBase) from which    "to project" the current pdf. Result PDF substituted the currently  stored one in the object.   C++: mrpt::poses::CPosePDFParticles::changeCoordinatesReference(const class mrpt::poses::CPose3D &) --> void clear(...) clear(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> None   Free all the memory associated to m_particles, and set the number of  parts = 0    C++: mrpt::poses::CPosePDFParticles::clear() --> void clone(...) clone(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::poses::CPosePDFParticles::clone() const --> class mrpt::rtti::CObject * copyFrom(...) copyFrom(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, o: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   Copy operator, translating if necesary (for example, between m_particles  and gaussian representations)   C++: mrpt::poses::CPosePDFParticles::copyFrom(const class mrpt::poses::CPosePDF &) --> void drawSingleSample(...) drawSingleSample(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, outPart: mrpt.pymrpt.mrpt.poses.CPose2D) -> None   Draws a single sample from the distribution (WARNING: weights are  assumed to be normalized!)   C++: mrpt::poses::CPosePDFParticles::drawSingleSample(class mrpt::poses::CPose2D &) const --> void evaluatePDF_parzen(...) evaluatePDF_parzen(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, x: float, y: float, phi: float, stdXY: float, stdPhi: float) -> float   Evaluates the PDF at a given arbitrary point as reconstructed by a  Parzen window.      saveParzenPDFToTextFile   C++: mrpt::poses::CPosePDFParticles::evaluatePDF_parzen(const double, const double, const double, const double, const double) const --> double getCovarianceAndMean(...) getCovarianceAndMean(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> Tuple[mrpt::math::CMatrixFixed<double, 3ul, 3ul>, mrpt.pymrpt.mrpt.poses.CPose2D]   C++: mrpt::poses::CPosePDFParticles::getCovarianceAndMean() const --> class std::tuple<class mrpt::math::CMatrixFixed<double, 3, 3>, class mrpt::poses::CPose2D> getMean(...) getMean(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, mean_pose: mrpt.pymrpt.mrpt.poses.CPose2D) -> None   C++: mrpt::poses::CPosePDFParticles::getMean(class mrpt::poses::CPose2D &) const --> void getMostLikelyParticle(...) getMostLikelyParticle(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> mrpt.pymrpt.mrpt.math.TPose2D   Returns the particle with the highest weight.   C++: mrpt::poses::CPosePDFParticles::getMostLikelyParticle() const --> struct mrpt::math::TPose2D getParticlePose(...) getParticlePose(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, i: int) -> mrpt.pymrpt.mrpt.math.TPose2D   Returns the pose of the i'th particle.   C++: mrpt::poses::CPosePDFParticles::getParticlePose(size_t) const --> struct mrpt::math::TPose2D inverse(...) inverse(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, o: mrpt.pymrpt.mrpt.poses.CPosePDF) -> None   Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF   C++: mrpt::poses::CPosePDFParticles::inverse(class mrpt::poses::CPosePDF &) const --> void resetDeterministic(...) resetDeterministic(*args, **kwargs) Overloaded function.   1. resetDeterministic(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, location: mrpt.pymrpt.mrpt.math.TPose2D) -> None   2. resetDeterministic(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, location: mrpt.pymrpt.mrpt.math.TPose2D, particlesCount: int) -> None   Reset the PDF to a single point: All m_particles will be set exactly to  the supplied pose.      The location to set all the m_particles.      If this is set to 0 the number of m_particles  remains unchanged.       resetUniform, CMonteCarloLocalization2D::resetUniformFreeSpace,  resetAroundSetOfPoses   C++: mrpt::poses::CPosePDFParticles::resetDeterministic(const struct mrpt::math::TPose2D &, size_t) --> void resetUniform(...) resetUniform(*args, **kwargs) Overloaded function.   1. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, x_min: float, x_max: float, y_min: float, y_max: float) -> None   2. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, x_min: float, x_max: float, y_min: float, y_max: float, phi_min: float) -> None   3. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, x_min: float, x_max: float, y_min: float, y_max: float, phi_min: float, phi_max: float) -> None   4. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, x_min: float, x_max: float, y_min: float, y_max: float, phi_min: float, phi_max: float, particlesCount: int) -> None   Reset the PDF to an uniformly distributed one, inside of the defined  2D area `[x_min,x_max]x[y_min,y_max]` (in meters) and for  orientations `[phi_min, phi_max]` (in radians).      New particle count, or leave count unchanged if set  to -1 (default).      Orientations can be outside of the [-pi,pi] range if so desired,        but it must hold `phi_max>=phi_min`.      resetDeterministic, CMonteCarloLocalization2D::resetUniformFreeSpace,  resetAroundSetOfPoses   C++: mrpt::poses::CPosePDFParticles::resetUniform(const double, const double, const double, const double, const double, const double, const int) --> void saveParzenPDFToTextFile(...) saveParzenPDFToTextFile(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, fileName: str, x_min: float, x_max: float, y_min: float, y_max: float, phi: float, stepSizeXY: float, stdXY: float, stdPhi: float) -> None   Save a text file (compatible with matlab) representing the 2D evaluation  of the PDF as reconstructed by a Parzen window.      evaluatePDF_parzen   C++: mrpt::poses::CPosePDFParticles::saveParzenPDFToTextFile(const char *, const double, const double, const double, const double, const double, const double, const double, const double) const --> void saveToTextFile(...) saveToTextFile(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles, file: str) -> bool   Save PDF's m_particles to a text file. In each line it will go: "x y phi  weight"   C++: mrpt::poses::CPosePDFParticles::saveToTextFile(const std::string &) const --> bool size(...) size(self: mrpt.pymrpt.mrpt.poses.CPosePDFParticles) -> int   Get the m_particles count (equivalent to "particlesCount")   C++: mrpt::poses::CPosePDFParticles::size() const --> size_t Static methods inherited from mrpt.pymrpt.mrpt.poses.CPosePDFParticles: CreateObject(...) from builtins.PyCapsule CreateObject() -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::poses::CPosePDFParticles::CreateObject() --> class std::shared_ptr<class mrpt::rtti::CObject> GetRuntimeClassIdStatic(...) from builtins.PyCapsule GetRuntimeClassIdStatic() -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::poses::CPosePDFParticles::GetRuntimeClassIdStatic() --> const struct mrpt::rtti::TRuntimeClassId & Static methods inherited from mrpt.pymrpt.mrpt.poses.CPosePDF: is_3D(...) from builtins.PyCapsule is_3D() -> bool   C++: mrpt::poses::CPosePDF::is_3D() --> bool is_PDF(...) from builtins.PyCapsule is_PDF() -> bool   C++: mrpt::poses::CPosePDF::is_PDF() --> bool jacobiansPoseComposition(...) from builtins.PyCapsule jacobiansPoseComposition(*args, **kwargs) Overloaded function.   1. jacobiansPoseComposition(x: mrpt.pymrpt.mrpt.poses.CPose2D, u: mrpt.pymrpt.mrpt.poses.CPose2D, df_dx: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, df_du: mrpt::math::CMatrixFixed<double, 3ul, 3ul>) -> None   2. jacobiansPoseComposition(x: mrpt.pymrpt.mrpt.poses.CPose2D, u: mrpt.pymrpt.mrpt.poses.CPose2D, df_dx: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, df_du: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, compute_df_dx: bool) -> None   3. jacobiansPoseComposition(x: mrpt.pymrpt.mrpt.poses.CPose2D, u: mrpt.pymrpt.mrpt.poses.CPose2D, df_dx: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, df_du: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, compute_df_dx: bool, compute_df_du: bool) -> None   This static method computes the pose composition Jacobians, with these            formulas:                                                     C++: mrpt::poses::CPosePDF::jacobiansPoseComposition(const class mrpt::poses::CPose2D &, const class mrpt::poses::CPose2D &, class mrpt::math::CMatrixFixed<double, 3, 3> &, class mrpt::math::CMatrixFixed<double, 3, 3> &, const bool, const bool) --> void   4. jacobiansPoseComposition(x: mrpt::poses::CPosePDFGaussian, u: mrpt::poses::CPosePDFGaussian, df_dx: mrpt::math::CMatrixFixed<double, 3ul, 3ul>, df_du: mrpt::math::CMatrixFixed<double, 3ul, 3ul>) -> None   C++: mrpt::poses::CPosePDF::jacobiansPoseComposition(const class mrpt::poses::CPosePDFGaussian &, const class mrpt::poses::CPosePDFGaussian &, class mrpt::math::CMatrixFixed<double, 3, 3> &, class mrpt::math::CMatrixFixed<double, 3, 3> &) --> void Methods inherited from mrpt.pymrpt.mrpt.rtti.CObject: duplicateGetSmartPtr(...) duplicateGetSmartPtr(self: mrpt.pymrpt.mrpt.rtti.CObject) -> mrpt.pymrpt.mrpt.rtti.CObject   Makes a deep copy of the object and returns a smart pointer to it    C++: mrpt::rtti::CObject::duplicateGetSmartPtr() const --> class std::shared_ptr<class mrpt::rtti::CObject> Methods inherited from mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t: getCovariance(...) getCovariance(*args, **kwargs) Overloaded function.   1. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t, cov: mrpt::math::CMatrixDynamic<double>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getCovariance(class mrpt::math::CMatrixDynamic<double> &) const --> void   2. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t, cov: mrpt::math::CMatrixFixed<double, 3ul, 3ul>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getCovariance(class mrpt::math::CMatrixFixed<double, 3, 3> &) const --> void   3. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t) -> mrpt::math::CMatrixFixed<double, 3ul, 3ul>   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getCovariance() const --> class mrpt::math::CMatrixFixed<double, 3, 3> getCovarianceDynAndMean(...) getCovarianceDynAndMean(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t, cov: mrpt::math::CMatrixDynamic<double>, mean_point: mrpt.pymrpt.mrpt.poses.CPose2D) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getCovarianceDynAndMean(class mrpt::math::CMatrixDynamic<double> &, class mrpt::poses::CPose2D &) const --> void getCovarianceEntropy(...) getCovarianceEntropy(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t) -> float   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getCovarianceEntropy() const --> double getInformationMatrix(...) getInformationMatrix(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t, inf: mrpt::math::CMatrixFixed<double, 3ul, 3ul>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getInformationMatrix(class mrpt::math::CMatrixFixed<double, 3, 3> &) const --> void getMeanVal(...) getMeanVal(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t) -> mrpt.pymrpt.mrpt.poses.CPose2D   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::getMeanVal() const --> class mrpt::poses::CPose2D isInfType(...) isInfType(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose2D_3UL_t) -> bool   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose2D, 3>::isInfType() const --> bool Methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t: clearParticles(...) clearParticles(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None   C++: mrpt::bayes::CParticleFilterData<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>::clearParticles() --> void Data descriptors inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t: m_particles Methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t: ESS(...) ESS(*args, **kwargs) Overloaded function.   1. ESS(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::ESS() const --> double   2. ESS(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   Returns the normalized ESS (Estimated Sample Size), in the range [0,1].   Note that you do NOT need to normalize the weights before calling this.   C++: mrpt::bayes::CParticleFilterCapable::ESS() const --> double derived(...) derived(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> mrpt::poses::CPosePDFParticles   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::derived() --> class mrpt::poses::CPosePDFParticles & fastDrawSample(...) fastDrawSample(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> int   Draws a random sample from the particle filter, in such a way that each particle has a probability proportional to its weight (in the standard PF algorithm).    This method can be used to generate a variable number of m_particles when resampling: to vary the number of m_particles in the filter.    See prepareFastDrawSample for more information, or the  *href="http://www.mrpt.org/Particle_Filters" >Particle Filter tutorial.    NOTES:                 - You MUST call "prepareFastDrawSample" ONCE before calling this method. That method must be called after modifying the particle filter (executing one step, resampling, etc...)                 - This method returns ONE index for the selected ("drawn") particle, in the range [0,M-1]                 - You do not need to call "normalizeWeights" before calling this.      prepareFastDrawSample   C++: mrpt::bayes::CParticleFilterCapable::fastDrawSample(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) const --> size_t getW(...) getW(*args, **kwargs) Overloaded function.   1. getW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::getW(size_t) const --> double   2. getW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int) -> float   Access to i'th particle (logarithm) weight, where first one is index 0.   C++: mrpt::bayes::CParticleFilterCapable::getW(size_t) const --> double normalizeWeights(...) normalizeWeights(*args, **kwargs) Overloaded function.   1. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   2. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, out_max_log_w: float) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::normalizeWeights(double *) --> double   3. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable) -> float   4. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, out_max_log_w: float) -> float   Normalize the (logarithmic) weights, such as the maximum weight is zero.      If provided, will return with the maximum log_w  before normalizing, such as new_weights = old_weights - max_log_w.      The max/min ratio of weights ("dynamic range")   C++: mrpt::bayes::CParticleFilterCapable::normalizeWeights(double *) --> double particlesCount(...) particlesCount(*args, **kwargs) Overloaded function.   1. particlesCount(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> int   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::particlesCount() const --> size_t   2. particlesCount(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> int   Get the m_particles count.   C++: mrpt::bayes::CParticleFilterCapable::particlesCount() const --> size_t performResampling(...) performResampling(*args, **kwargs) Overloaded function.   1. performResampling(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   2. performResampling(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, out_particle_count: int) -> None   Performs a resample of the m_particles, using the method selected in the  constructor.  After computing the surviving samples, this method internally calls  "performSubstitution" to actually perform the particle replacement.  This method is called automatically by CParticleFilter::execute,  andshould not be invoked manually normally.  To just obtaining the sequence of resampled indexes from a sequence of  weights, use "resample"      The desired number of output particles  after resampling; 0 means don't modify the current number.      resample   C++: mrpt::bayes::CParticleFilterCapable::performResampling(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t) --> void prediction_and_update(...) prediction_and_update(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Performs the prediction stage of the Particle Filter.   This method simply selects the appropiate protected method according to  the particle filter algorithm to run.        prediction_and_update_pfStandardProposal,prediction_and_update_pfAuxiliaryPFStandard,prediction_and_update_pfOptimalProposal,prediction_and_update_pfAuxiliaryPFOptimal   C++: mrpt::bayes::CParticleFilterCapable::prediction_and_update(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void setW(...) setW(*args, **kwargs) Overloaded function.   1. setW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, w: float) -> None   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPosePDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>>>::setW(size_t, double) --> void   2. setW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, w: float) -> None   Modifies i'th particle (logarithm) weight, where first one is index 0.   C++: mrpt::bayes::CParticleFilterCapable::setW(size_t, double) --> void Static methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPosePDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose2D_mrpt_bayes_particle_storage_mode_VALUE_t: defaultEvaluator(...) from builtins.PyCapsule defaultEvaluator(PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, obj: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable, index: int, action: capsule, observation: capsule) -> float   The default evaluator function, which simply returns the particle  weight.   The action and the observation are declared as "void*" for a greater  flexibility.      prepareFastDrawSample   C++: mrpt::bayes::CParticleFilterCapable::defaultEvaluator(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, const class mrpt::bayes::CParticleFilterCapable *, size_t, const void *, const void *) --> double Methods inherited from PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t: PF_SLAM_implementation_doWeHaveValidObservations(...) PF_SLAM_implementation_doWeHaveValidObservations(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, particles: List[mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, (mrpt::bayes::particle_storage_mode)0>], sf: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_doWeHaveValidObservations(const class std::deque<struct mrpt::bayes::CProbabilityParticle<struct mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE> > &, const class mrpt::obs::CSensoryFrame *) const --> bool PF_SLAM_implementation_skipRobotMovement(...) PF_SLAM_implementation_skipRobotMovement(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_skipRobotMovement() const --> bool Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CMonteCarloLocalization3D(mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t)     Declares a class that represents a Probability Density Function (PDF) over a 3D pose (x,y,phi,yaw,pitch,roll), using a set of weighted samples.    This class also implements particle filtering for robot localization. See the MRPT   application "app/pf-localization" for an example of usage.     CMonteCarloLocalization2D, CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable     Method resolution order: CMonteCarloLocalization3D mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles mrpt.pymrpt.mrpt.poses.CPose3DPDF mrpt.pymrpt.mrpt.serialization.CSerializable mrpt.pymrpt.mrpt.rtti.CObject mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable mrpt.pymrpt.mrpt.Stringifyable PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t pybind11_builtins.pybind11_object builtins.object Methods defined here: PF_SLAM_computeObservationLikelihoodForParticle(...) PF_SLAM_computeObservationLikelihoodForParticle(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, particleIndexForMap: int, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, x: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Evaluate the observation likelihood for one particle at a given location   C++: mrpt::slam::CMonteCarloLocalization3D::PF_SLAM_computeObservationLikelihoodForParticle(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D &) const --> double PF_SLAM_implementation_custom_update_particle_with_new_pose(...) PF_SLAM_implementation_custom_update_particle_with_new_pose(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, particleData: mrpt.pymrpt.mrpt.math.TPose3D, newPose: mrpt.pymrpt.mrpt.math.TPose3D) -> None   C++: mrpt::slam::CMonteCarloLocalization3D::PF_SLAM_implementation_custom_update_particle_with_new_pose(struct mrpt::math::TPose3D *, const struct mrpt::math::TPose3D &) const --> void __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D) -> None   doc   2. __init__(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, M: int) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, : mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D) -> mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D   C++: mrpt::slam::CMonteCarloLocalization3D::operator=(const class mrpt::slam::CMonteCarloLocalization3D &) --> class mrpt::slam::CMonteCarloLocalization3D & getLastPose(...) getLastPose(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, i: int, is_valid_pose: bool) -> mrpt.pymrpt.mrpt.math.TPose3D                   @{     Return the robot pose for the i'th particle. is_valid is  always true in this class.    C++: mrpt::slam::CMonteCarloLocalization3D::getLastPose(size_t, bool &) const --> struct mrpt::math::TPose3D getVisualization(...) getVisualization(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D) -> mrpt.pymrpt.mrpt.opengl.CSetOfObjects   Returns a 3D representation of this PDF.      Needs the mrpt-opengl library, and using  mrpt::opengl::CSetOfObjects::Ptr as template argument.   C++: mrpt::slam::CMonteCarloLocalization3D::getVisualization() const --> class std::shared_ptr<class mrpt::opengl::CSetOfObjects> prediction_and_update_pfAuxiliaryPFOptimal(...) prediction_and_update_pfAuxiliaryPFOptimal(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization3D::prediction_and_update_pfAuxiliaryPFOptimal(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void prediction_and_update_pfAuxiliaryPFStandard(...) prediction_and_update_pfAuxiliaryPFStandard(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization3D::prediction_and_update_pfAuxiliaryPFStandard(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void prediction_and_update_pfStandardProposal(...) prediction_and_update_pfStandardProposal(self: mrpt.pymrpt.mrpt.slam.CMonteCarloLocalization3D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Update the m_particles, predicting the posterior of robot pose and map  after a movement command.   This method has additional configuration parameters in "options".   Performs the update stage of the RBPF, using the sensed CSensoryFrame:        This is a pointer to CActionCollection, containing the  pose change the robot has been commanded.        This must be a pointer to a CSensoryFrame object,  with robot sensed observations.      options   C++: mrpt::slam::CMonteCarloLocalization3D::prediction_and_update_pfStandardProposal(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void Data descriptors defined here: options Methods inherited from mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles: GetRuntimeClass(...) GetRuntimeClass(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::poses::CPose3DPDFParticles::GetRuntimeClass() const --> const struct mrpt::rtti::TRuntimeClassId * __iadd__(...) __iadd__(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, Ap: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   Appends (pose-composition) a given pose "p" to each particle    C++: mrpt::poses::CPose3DPDFParticles::operator+=(const class mrpt::poses::CPose3D &) --> void append(...) append(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, o: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> None   Appends (add to the list) a set of m_particles to the existing ones, and  then normalize weights.    C++: mrpt::poses::CPose3DPDFParticles::append(class mrpt::poses::CPose3DPDFParticles &) --> void asString(...) asString(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> str   C++: mrpt::poses::CPose3DPDFParticles::asString() const --> std::string bayesianFusion(...) bayesianFusion(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, p1: mrpt.pymrpt.mrpt.poses.CPose3DPDF, p2: mrpt.pymrpt.mrpt.poses.CPose3DPDF) -> None   Bayesian fusion    C++: mrpt::poses::CPose3DPDFParticles::bayesianFusion(const class mrpt::poses::CPose3DPDF &, const class mrpt::poses::CPose3DPDF &) --> void changeCoordinatesReference(...) changeCoordinatesReference(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, newReferenceBase: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   this = p (+) this. This can be used to convert a PDF from local  coordinates to global, providing the point (newReferenceBase) from which    "to project" the current pdf. Result PDF substituted the currently  stored one in the object.    C++: mrpt::poses::CPose3DPDFParticles::changeCoordinatesReference(const class mrpt::poses::CPose3D &) --> void clone(...) clone(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::poses::CPose3DPDFParticles::clone() const --> class mrpt::rtti::CObject * copyFrom(...) copyFrom(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, o: mrpt.pymrpt.mrpt.poses.CPose3DPDF) -> None   Copy operator, translating if necesary (for example, between m_particles  and gaussian representations)    C++: mrpt::poses::CPose3DPDFParticles::copyFrom(const class mrpt::poses::CPose3DPDF &) --> void drawSingleSample(...) drawSingleSample(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, outPart: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   Draws a single sample from the distribution (WARNING: weights are  assumed to be normalized!)    C++: mrpt::poses::CPose3DPDFParticles::drawSingleSample(class mrpt::poses::CPose3D &) const --> void getCovarianceAndMean(...) getCovarianceAndMean(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> Tuple[mrpt::math::CMatrixFixed<double, 6ul, 6ul>, mrpt.pymrpt.mrpt.poses.CPose3D]   Returns an estimate of the pose covariance matrix (6x6 cov matrix) and  the mean, both at once.     getMean    C++: mrpt::poses::CPose3DPDFParticles::getCovarianceAndMean() const --> class std::tuple<class mrpt::math::CMatrixFixed<double, 6, 6>, class mrpt::poses::CPose3D> getMean(...) getMean(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, mean_pose: mrpt.pymrpt.mrpt.poses.CPose3D) -> None   Returns an estimate of the pose, (the mean, or mathematical expectation  of the PDF), computed as a weighted average over all m_particles.       getCovariance    C++: mrpt::poses::CPose3DPDFParticles::getMean(class mrpt::poses::CPose3D &) const --> void getMostLikelyParticle(...) getMostLikelyParticle(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> mrpt.pymrpt.mrpt.math.TPose3D   Returns the particle with the highest weight.    C++: mrpt::poses::CPose3DPDFParticles::getMostLikelyParticle() const --> struct mrpt::math::TPose3D getParticlePose(...) getParticlePose(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, i: int) -> mrpt.pymrpt.mrpt.math.TPose3D   Returns the pose of the i'th particle    C++: mrpt::poses::CPose3DPDFParticles::getParticlePose(int) const --> struct mrpt::math::TPose3D inverse(...) inverse(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, o: mrpt.pymrpt.mrpt.poses.CPose3DPDF) -> None   Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF    C++: mrpt::poses::CPose3DPDFParticles::inverse(class mrpt::poses::CPose3DPDF &) const --> void resetDeterministic(...) resetDeterministic(*args, **kwargs) Overloaded function.   1. resetDeterministic(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, location: mrpt.pymrpt.mrpt.math.TPose3D) -> None   2. resetDeterministic(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, location: mrpt.pymrpt.mrpt.math.TPose3D, particlesCount: int) -> None   Reset the PDF to a single point: All m_particles will be set exactly to  the supplied pose.      The location to set all the m_particles.      If this is set to 0 the number of m_particles  remains unchanged.       resetUniform    C++: mrpt::poses::CPose3DPDFParticles::resetDeterministic(const struct mrpt::math::TPose3D &, size_t) --> void resetUniform(...) resetUniform(*args, **kwargs) Overloaded function.   1. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, corner_min: mrpt.pymrpt.mrpt.math.TPose3D, corner_max: mrpt.pymrpt.mrpt.math.TPose3D) -> None   2. resetUniform(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, corner_min: mrpt.pymrpt.mrpt.math.TPose3D, corner_max: mrpt.pymrpt.mrpt.math.TPose3D, particlesCount: int) -> None   Reset the PDF to an uniformly distributed one, inside of the defined  "cube".      New particle count, or leave count unchanged if set  to -1 (default).      Orientations can be outside of the [-pi,pi] range if so desired,        but it must hold `phi_max>=phi_min`.      resetDeterministic  resetAroundSetOfPoses   C++: mrpt::poses::CPose3DPDFParticles::resetUniform(const struct mrpt::math::TPose3D &, const struct mrpt::math::TPose3D &, const int) --> void saveToTextFile(...) saveToTextFile(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles, file: str) -> bool   Save PDF's m_particles to a text file. In each line it will go: "x y z"   C++: mrpt::poses::CPose3DPDFParticles::saveToTextFile(const std::string &) const --> bool size(...) size(self: mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles) -> int   Get the m_particles count (equivalent to "particlesCount")    C++: mrpt::poses::CPose3DPDFParticles::size() const --> size_t Static methods inherited from mrpt.pymrpt.mrpt.poses.CPose3DPDFParticles: CreateObject(...) from builtins.PyCapsule CreateObject() -> mrpt.pymrpt.mrpt.rtti.CObject   C++: mrpt::poses::CPose3DPDFParticles::CreateObject() --> class std::shared_ptr<class mrpt::rtti::CObject> GetRuntimeClassIdStatic(...) from builtins.PyCapsule GetRuntimeClassIdStatic() -> mrpt.pymrpt.mrpt.rtti.TRuntimeClassId   C++: mrpt::poses::CPose3DPDFParticles::GetRuntimeClassIdStatic() --> const struct mrpt::rtti::TRuntimeClassId & Static methods inherited from mrpt.pymrpt.mrpt.poses.CPose3DPDF: createFrom2D(...) from builtins.PyCapsule createFrom2D(o: mrpt.pymrpt.mrpt.poses.CPosePDF) -> mrpt.pymrpt.mrpt.poses.CPose3DPDF   This is a static transformation method from 2D poses to 3D PDFs,  preserving the representation type (particles->particles,  Gaussians->Gaussians,etc)      It returns a new object of any of the derived classes of  CPose3DPDF. This object must be deleted by the user when not required  anymore.      copyFrom   C++: mrpt::poses::CPose3DPDF::createFrom2D(const class mrpt::poses::CPosePDF &) --> class mrpt::poses::CPose3DPDF * is_3D(...) from builtins.PyCapsule is_3D() -> bool   C++: mrpt::poses::CPose3DPDF::is_3D() --> bool is_PDF(...) from builtins.PyCapsule is_PDF() -> bool   C++: mrpt::poses::CPose3DPDF::is_PDF() --> bool jacobiansPoseComposition(...) from builtins.PyCapsule jacobiansPoseComposition(x: mrpt.pymrpt.mrpt.poses.CPose3D, u: mrpt.pymrpt.mrpt.poses.CPose3D, df_dx: mrpt::math::CMatrixFixed<double, 6ul, 6ul>, df_du: mrpt::math::CMatrixFixed<double, 6ul, 6ul>) -> None   This static method computes the pose composition Jacobians.    See this techical report:  http:///www.mrpt.org/6D_poses:equivalences_compositions_and_uncertainty    Direct equations (for the covariances) in yaw-pitch-roll are too complex.   Make a way around them and consider instead this path:                                           C++: mrpt::poses::CPose3DPDF::jacobiansPoseComposition(const class mrpt::poses::CPose3D &, const class mrpt::poses::CPose3D &, class mrpt::math::CMatrixFixed<double, 6, 6> &, class mrpt::math::CMatrixFixed<double, 6, 6> &) --> void Methods inherited from mrpt.pymrpt.mrpt.rtti.CObject: duplicateGetSmartPtr(...) duplicateGetSmartPtr(self: mrpt.pymrpt.mrpt.rtti.CObject) -> mrpt.pymrpt.mrpt.rtti.CObject   Makes a deep copy of the object and returns a smart pointer to it    C++: mrpt::rtti::CObject::duplicateGetSmartPtr() const --> class std::shared_ptr<class mrpt::rtti::CObject> Methods inherited from mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t: getCovariance(...) getCovariance(*args, **kwargs) Overloaded function.   1. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t, cov: mrpt::math::CMatrixDynamic<double>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getCovariance(class mrpt::math::CMatrixDynamic<double> &) const --> void   2. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t, cov: mrpt::math::CMatrixFixed<double, 6ul, 6ul>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getCovariance(class mrpt::math::CMatrixFixed<double, 6, 6> &) const --> void   3. getCovariance(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t) -> mrpt::math::CMatrixFixed<double, 6ul, 6ul>   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getCovariance() const --> class mrpt::math::CMatrixFixed<double, 6, 6> getCovarianceDynAndMean(...) getCovarianceDynAndMean(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t, cov: mrpt::math::CMatrixDynamic<double>, mean_point: mrpt::poses::CPose3D) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getCovarianceDynAndMean(class mrpt::math::CMatrixDynamic<double> &, class mrpt::poses::CPose3D &) const --> void getCovarianceEntropy(...) getCovarianceEntropy(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t) -> float   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getCovarianceEntropy() const --> double getInformationMatrix(...) getInformationMatrix(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t, inf: mrpt::math::CMatrixFixed<double, 6ul, 6ul>) -> None   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getInformationMatrix(class mrpt::math::CMatrixFixed<double, 6, 6> &) const --> void getMeanVal(...) getMeanVal(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t) -> mrpt::poses::CPose3D   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::getMeanVal() const --> class mrpt::poses::CPose3D isInfType(...) isInfType(self: mrpt.pymrpt.mrpt.math.CProbabilityDensityFunction_mrpt_poses_CPose3D_6UL_t) -> bool   C++: mrpt::math::CProbabilityDensityFunction<mrpt::poses::CPose3D, 6>::isInfType() const --> bool Methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t: clearParticles(...) clearParticles(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None   C++: mrpt::bayes::CParticleFilterData<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>::clearParticles() --> void Data descriptors inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterData_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t: m_particles Methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t: ESS(...) ESS(*args, **kwargs) Overloaded function.   1. ESS(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::ESS() const --> double   2. ESS(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   Returns the normalized ESS (Estimated Sample Size), in the range [0,1].   Note that you do NOT need to normalize the weights before calling this.   C++: mrpt::bayes::CParticleFilterCapable::ESS() const --> double derived(...) derived(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> mrpt::poses::CPose3DPDFParticles   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::derived() --> class mrpt::poses::CPose3DPDFParticles & fastDrawSample(...) fastDrawSample(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> int   Draws a random sample from the particle filter, in such a way that each particle has a probability proportional to its weight (in the standard PF algorithm).    This method can be used to generate a variable number of m_particles when resampling: to vary the number of m_particles in the filter.    See prepareFastDrawSample for more information, or the  *href="http://www.mrpt.org/Particle_Filters" >Particle Filter tutorial.    NOTES:                 - You MUST call "prepareFastDrawSample" ONCE before calling this method. That method must be called after modifying the particle filter (executing one step, resampling, etc...)                 - This method returns ONE index for the selected ("drawn") particle, in the range [0,M-1]                 - You do not need to call "normalizeWeights" before calling this.      prepareFastDrawSample   C++: mrpt::bayes::CParticleFilterCapable::fastDrawSample(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) const --> size_t getW(...) getW(*args, **kwargs) Overloaded function.   1. getW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::getW(size_t) const --> double   2. getW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int) -> float   Access to i'th particle (logarithm) weight, where first one is index 0.   C++: mrpt::bayes::CParticleFilterCapable::getW(size_t) const --> double normalizeWeights(...) normalizeWeights(*args, **kwargs) Overloaded function.   1. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> float   2. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, out_max_log_w: float) -> float   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::normalizeWeights(double *) --> double   3. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable) -> float   4. normalizeWeights(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, out_max_log_w: float) -> float   Normalize the (logarithmic) weights, such as the maximum weight is zero.      If provided, will return with the maximum log_w  before normalizing, such as new_weights = old_weights - max_log_w.      The max/min ratio of weights ("dynamic range")   C++: mrpt::bayes::CParticleFilterCapable::normalizeWeights(double *) --> double particlesCount(...) particlesCount(*args, **kwargs) Overloaded function.   1. particlesCount(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> int   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::particlesCount() const --> size_t   2. particlesCount(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> int   Get the m_particles count.   C++: mrpt::bayes::CParticleFilterCapable::particlesCount() const --> size_t performResampling(...) performResampling(*args, **kwargs) Overloaded function.   1. performResampling(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   2. performResampling(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, out_particle_count: int) -> None   Performs a resample of the m_particles, using the method selected in the  constructor.  After computing the surviving samples, this method internally calls  "performSubstitution" to actually perform the particle replacement.  This method is called automatically by CParticleFilter::execute,  andshould not be invoked manually normally.  To just obtaining the sequence of resampled indexes from a sequence of  weights, use "resample"      The desired number of output particles  after resampling; 0 means don't modify the current number.      resample   C++: mrpt::bayes::CParticleFilterCapable::performResampling(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t) --> void prediction_and_update(...) prediction_and_update(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, action: mrpt.pymrpt.mrpt.obs.CActionCollection, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions) -> None   Performs the prediction stage of the Particle Filter.   This method simply selects the appropiate protected method according to  the particle filter algorithm to run.        prediction_and_update_pfStandardProposal,prediction_and_update_pfAuxiliaryPFStandard,prediction_and_update_pfOptimalProposal,prediction_and_update_pfAuxiliaryPFOptimal   C++: mrpt::bayes::CParticleFilterCapable::prediction_and_update(const class mrpt::obs::CActionCollection *, const class mrpt::obs::CSensoryFrame *, const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &) --> void setW(...) setW(*args, **kwargs) Overloaded function.   1. setW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, w: float) -> None   C++: mrpt::bayes::CParticleFilterDataImpl<mrpt::poses::CPose3DPDFParticles, std::deque<mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>>>::setW(size_t, double) --> void   2. setW(self: mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, w: float) -> None   Modifies i'th particle (logarithm) weight, where first one is index 0.   C++: mrpt::bayes::CParticleFilterCapable::setW(size_t, double) --> void Static methods inherited from mrpt.pymrpt.mrpt.bayes.CParticleFilterDataImpl_mrpt_poses_CPose3DPDFParticles_std_deque_mrpt_bayes_CProbabilityParticle_mrpt_math_TPose3D_mrpt_bayes_particle_storage_mode_VALUE_t: defaultEvaluator(...) from builtins.PyCapsule defaultEvaluator(PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, obj: mrpt.pymrpt.mrpt.bayes.CParticleFilterCapable, index: int, action: capsule, observation: capsule) -> float   The default evaluator function, which simply returns the particle  weight.   The action and the observation are declared as "void*" for a greater  flexibility.      prepareFastDrawSample   C++: mrpt::bayes::CParticleFilterCapable::defaultEvaluator(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, const class mrpt::bayes::CParticleFilterCapable *, size_t, const void *, const void *) --> double Methods inherited from PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t: PF_SLAM_implementation_doWeHaveValidObservations(...) PF_SLAM_implementation_doWeHaveValidObservations(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, particles: List[mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, (mrpt::bayes::particle_storage_mode)0>], sf: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_doWeHaveValidObservations(const class std::deque<struct mrpt::bayes::CProbabilityParticle<struct mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE> > &, const class mrpt::obs::CSensoryFrame *) const --> bool PF_SLAM_implementation_skipRobotMovement(...) PF_SLAM_implementation_skipRobotMovement(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_skipRobotMovement() const --> bool Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class COccupancyGridMapFeatureExtractor(mrpt.pymrpt.mrpt.system.CObserver)     A class for detecting features from occupancy grid maps.   The main method is "COccupancyGridMapFeatureExtractor::extractFeatures()", which makes use    of an advanced cache mechanism to avoid redoing work when applied several times on the same    occupancy grid maps (unless they changed in the meanwhile).    For an uncached version (which is a static method that can be called without instantiating COccupancyGridMapFeatureExtractor)  see COccupancyGridMapFeatureExtractor::uncached_extractFeatures()     Method resolution order: COccupancyGridMapFeatureExtractor mrpt.pymrpt.mrpt.system.CObserver pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor, arg0: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor, arg0: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor, : mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor) -> mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor   C++: mrpt::slam::COccupancyGridMapFeatureExtractor::operator=(const class mrpt::slam::COccupancyGridMapFeatureExtractor &) --> class mrpt::slam::COccupancyGridMapFeatureExtractor & extractFeatures(...) extractFeatures(self: mrpt.pymrpt.mrpt.slam.COccupancyGridMapFeatureExtractor, grid: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, outMap: mrpt.pymrpt.mrpt.maps.CLandmarksMap, number_of_features: int, descriptors: mrpt.pymrpt.mrpt.vision.TDescriptorType, feat_options: mrpt.pymrpt.mrpt.vision.CFeatureExtraction.TOptions) -> None   Computes a set of distinctive landmarks from an occupancy grid, and  store them (previous content is not erased!) into the given landmarks  map.    Landmarks type can be any declared in  mrpt::vision::CFeatureExtraction::TOptions      See the paper "..."      uncached_extractFeatures   C++: mrpt::slam::COccupancyGridMapFeatureExtractor::extractFeatures(const class mrpt::maps::COccupancyGridMap2D &, class mrpt::maps::CLandmarksMap &, size_t, const enum mrpt::vision::TDescriptorType, const struct mrpt::vision::CFeatureExtraction::TOptions &) --> void Static methods defined here: uncached_extractFeatures(...) from builtins.PyCapsule uncached_extractFeatures(grid: mrpt.pymrpt.mrpt.maps.COccupancyGridMap2D, outMap: mrpt.pymrpt.mrpt.maps.CLandmarksMap, number_of_features: int, descriptors: mrpt.pymrpt.mrpt.vision.TDescriptorType, feat_options: mrpt.pymrpt.mrpt.vision.CFeatureExtraction.TOptions) -> None   Computes a set of distinctive landmarks from an occupancy grid, and  store them (previous content is not erased!) into the given landmarks  map.    Landmarks type can be any declared in  mrpt::vision::CFeatureExtraction::TOptions      See the paper "..."      uncached_extractFeatures   C++: mrpt::slam::COccupancyGridMapFeatureExtractor::uncached_extractFeatures(const class mrpt::maps::COccupancyGridMap2D &, class mrpt::maps::CLandmarksMap &, size_t, const enum mrpt::vision::TDescriptorType, const struct mrpt::vision::CFeatureExtraction::TOptions &) --> void Methods inherited from mrpt.pymrpt.mrpt.system.CObserver: observeBegin(...) observeBegin(self: mrpt.pymrpt.mrpt.system.CObserver, obj: mrpt.pymrpt.mrpt.system.CObservable) -> None   Starts the subscription of this observer to the given object.      observeEnd     C++: mrpt::system::CObserver::observeBegin(class mrpt::system::CObservable &) --> void observeEnd(...) observeEnd(self: mrpt.pymrpt.mrpt.system.CObserver, obj: mrpt.pymrpt.mrpt.system.CObservable) -> None   Ends the subscription of this observer to the given object (note that            there is no need to call this method, since the destruction of the first            of observer/observed will put an end to the process                     observeBegin     C++: mrpt::system::CObserver::observeEnd(class mrpt::system::CObservable &) --> void Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CRangeBearingKFSLAM(mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t)     An implementation of EKF-based SLAM with range-bearing sensors, odometry, a SE(3) robot pose, and 3D landmarks. The main method is processActionObservation() which processes pairs of actions/observations.   The state vector comprises: 3D robot position, a quaternion for its attitude, and the 3D landmarks in the map.   The front-end application [kf-slam](page_app_kf-slam.html) is based on this class.   For the theory behind this implementation, see the technical report:       An implementation for 2D and SE(2) is in CRangeBearingKFSLAM2D     Method resolution order: CRangeBearingKFSLAM mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, arg0: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, arg0: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> None getAs3DObject(...) getAs3DObject(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, outObj: mrpt.pymrpt.mrpt.opengl.CSetOfObjects) -> None   Returns a 3D representation of the landmarks in the map and the robot 3D  position according to the current filter state.                    C++: mrpt::slam::CRangeBearingKFSLAM::getAs3DObject(class std::shared_ptr<class mrpt::opengl::CSetOfObjects> &) const --> void getCurrentRobotPose(...) getCurrentRobotPose(*args, **kwargs) Overloaded function.   1. getCurrentRobotPose(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, out_robotPose: mrpt.pymrpt.mrpt.poses.CPose3DQuatPDFGaussian) -> None   Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with  rotation as a quaternion).      getCurrentState, getCurrentRobotPoseMean   C++: mrpt::slam::CRangeBearingKFSLAM::getCurrentRobotPose(class mrpt::poses::CPose3DQuatPDFGaussian &) const --> void   2. getCurrentRobotPose(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, out_robotPose: mrpt.pymrpt.mrpt.poses.CPose3DPDFGaussian) -> None   Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with  rotation as 3 angles).      getCurrentState   C++: mrpt::slam::CRangeBearingKFSLAM::getCurrentRobotPose(class mrpt::poses::CPose3DPDFGaussian &) const --> void getCurrentRobotPoseMean(...) getCurrentRobotPoseMean(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> mrpt.pymrpt.mrpt.poses.CPose3DQuat   Get the current robot pose mean, as a 3D+quaternion pose.      getCurrentRobotPose   C++: mrpt::slam::CRangeBearingKFSLAM::getCurrentRobotPoseMean() const --> class mrpt::poses::CPose3DQuat getLastDataAssociation(...) getLastDataAssociation(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo   Returns a read-only reference to the information on the last  data-association    C++: mrpt::slam::CRangeBearingKFSLAM::getLastDataAssociation() const --> const struct mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo & loadOptions(...) loadOptions(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, ini: mrpt.pymrpt.mrpt.config.CConfigFileBase) -> None   Load options from a ini-like file/text   C++: mrpt::slam::CRangeBearingKFSLAM::loadOptions(const class mrpt::config::CConfigFileBase &) --> void mapPartitionOptions(...) mapPartitionOptions(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> mrpt.pymrpt.mrpt.slam.CIncrementalMapPartitioner.TOptions   Provides access to the parameters of the map partitioning algorithm.   C++: mrpt::slam::CRangeBearingKFSLAM::mapPartitionOptions() --> struct mrpt::slam::CIncrementalMapPartitioner::TOptions * processActionObservation(...) processActionObservation(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, action: mrpt.pymrpt.mrpt.obs.CActionCollection, SF: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> None   Process one new action and observations to update the map and robot pose estimate. See the description of the class at the top of this page.       May contain odometry             The set of observations, must contain at least one CObservationBearingRange   C++: mrpt::slam::CRangeBearingKFSLAM::processActionObservation(class std::shared_ptr<class mrpt::obs::CActionCollection> &, class std::shared_ptr<class mrpt::obs::CSensoryFrame> &) --> void reconsiderPartitionsNow(...) reconsiderPartitionsNow(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> None   The partitioning of the entire map is recomputed again.   Only when options.doPartitioningExperiment = true.   This can be used after changing the parameters of the partitioning  method.   After this method, you can call getLastPartitionLandmarks.      getLastPartitionLandmarks   C++: mrpt::slam::CRangeBearingKFSLAM::reconsiderPartitionsNow() --> void reset(...) reset(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM) -> None   Reset the state of the SLAM filter: The map is emptied and the robot put  back to (0,0,0).    C++: mrpt::slam::CRangeBearingKFSLAM::reset() --> void saveMapAndPath2DRepresentationAsMATLABFile(...) saveMapAndPath2DRepresentationAsMATLABFile(*args, **kwargs) Overloaded function.   1. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, fil: str) -> None   2. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, fil: str, stdCount: float) -> None   3. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, fil: str, stdCount: float, styleLandmarks: str) -> None   4. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, fil: str, stdCount: float, styleLandmarks: str, stylePath: str) -> None   5. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM, fil: str, stdCount: float, styleLandmarks: str, stylePath: str, styleRobot: str) -> None   Save the current state of the filter (robot pose & map) to a MATLAB  script which displays all the elements in 2D   C++: mrpt::slam::CRangeBearingKFSLAM::saveMapAndPath2DRepresentationAsMATLABFile(const std::string &, float, const std::string &, const std::string &, const std::string &) const --> void Data descriptors defined here: options Data and other attributes defined here: TDataAssocInfo = <class 'mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM.TDataAssocInfo'> Information for data-association:     getLastDataAssociation TOptions = <class 'mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM.TOptions'> The options for the algorithm Methods inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t: OnNewLandmarkAddedToMap(...) OnNewLandmarkAddedToMap(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t, in_obsIdx: int, in_idxNewFeat: int) -> None   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::OnNewLandmarkAddedToMap(size_t, size_t) --> void OnNormalizeStateVector(...) OnNormalizeStateVector(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::OnNormalizeStateVector() --> void OnPostIteration(...) OnPostIteration(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::OnPostIteration() --> void getLandmarkCov(...) getLandmarkCov(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t, idx: int, feat_cov: mrpt.pymrpt.mrpt.math.CMatrixFixed_double_3UL_3UL_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::getLandmarkCov(size_t, class mrpt::math::CMatrixFixed<double, 3, 3> &) const --> void getLandmarkMean(...) getLandmarkMean(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t, idx: int, feat: mrpt.pymrpt.mrpt.math.CMatrixFixed_double_3UL_1UL_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::getLandmarkMean(size_t, class mrpt::math::CMatrixFixed<double, 3, 1> &) const --> void getNumberOfLandmarksInTheMap(...) getNumberOfLandmarksInTheMap(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::getNumberOfLandmarksInTheMap() const --> size_t getProfiler(...) getProfiler(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> mrpt.pymrpt.mrpt.system.CTimeLogger   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::getProfiler() --> class mrpt::system::CTimeLogger & getStateVectorLength(...) getStateVectorLength(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::getStateVectorLength() const --> size_t internal_getPkk(...) internal_getPkk(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> mrpt.pymrpt.mrpt.math.CMatrixDynamic_double_t   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::internal_getPkk() --> class mrpt::math::CMatrixDynamic<double> & internal_getXkk(...) internal_getXkk(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> mrpt::math::CVectorDynamic<double>   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::internal_getXkk() --> class mrpt::math::CVectorDynamic<double> & isMapEmpty(...) isMapEmpty(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t) -> bool   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::isMapEmpty() const --> bool Static methods inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t: get_action_size(...) from builtins.PyCapsule get_action_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::get_action_size() --> size_t get_feature_size(...) from builtins.PyCapsule get_feature_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::get_feature_size() --> size_t get_observation_size(...) from builtins.PyCapsule get_observation_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::get_observation_size() --> size_t get_vehicle_size(...) from builtins.PyCapsule get_vehicle_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<7, 3, 3, 7>::get_vehicle_size() --> size_t Readonly properties inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_7UL_3UL_3UL_7UL_double_t: KF_options Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CRangeBearingKFSLAM2D(mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t)     An implementation of EKF-based SLAM with range-bearing sensors, odometry, SE(2) robot pose, and 2D landmarks. The main method is processActionObservation() which processes pairs of actions/observations.   The following front-end applications are based on this class:        - [2d-slam-demo](app_2d-slam-demo.html)        - [kf-slam](page_app_kf-slam.html)     CRangeBearingKFSLAM     Method resolution order: CRangeBearingKFSLAM2D mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, arg0: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, arg0: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D) -> None getAs3DObject(...) getAs3DObject(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, outObj: mrpt.pymrpt.mrpt.opengl.CSetOfObjects) -> None   Returns a 3D representation of the landmarks in the map and the robot 3D  position according to the current filter state.                    C++: mrpt::slam::CRangeBearingKFSLAM2D::getAs3DObject(class std::shared_ptr<class mrpt::opengl::CSetOfObjects> &) const --> void getCurrentRobotPose(...) getCurrentRobotPose(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, out_robotPose: mrpt.pymrpt.mrpt.poses.CPosePDFGaussian) -> None   Returns the mean & 3x3 covariance matrix of the robot 2D pose.      getCurrentState   C++: mrpt::slam::CRangeBearingKFSLAM2D::getCurrentRobotPose(class mrpt::poses::CPosePDFGaussian &) const --> void getLastDataAssociation(...) getLastDataAssociation(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D) -> mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo   Returns a read-only reference to the information on the last  data-association    C++: mrpt::slam::CRangeBearingKFSLAM2D::getLastDataAssociation() const --> const struct mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo & loadOptions(...) loadOptions(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, ini: mrpt.pymrpt.mrpt.config.CConfigFileBase) -> None   Load options from a ini-like file/text   C++: mrpt::slam::CRangeBearingKFSLAM2D::loadOptions(const class mrpt::config::CConfigFileBase &) --> void processActionObservation(...) processActionObservation(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, action: mrpt.pymrpt.mrpt.obs.CActionCollection, SF: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> None   Process one new action and observations to update the map and robot pose estimate. See the description of the class at the top of this page.       May contain odometry             The set of observations, must contain at least one CObservationBearingRange   C++: mrpt::slam::CRangeBearingKFSLAM2D::processActionObservation(class std::shared_ptr<class mrpt::obs::CActionCollection> &, class std::shared_ptr<class mrpt::obs::CSensoryFrame> &) --> void reset(...) reset(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D) -> None   Reset the state of the SLAM filter: The map is emptied and the robot put  back to (0,0,0).    C++: mrpt::slam::CRangeBearingKFSLAM2D::reset() --> void saveMapAndPath2DRepresentationAsMATLABFile(...) saveMapAndPath2DRepresentationAsMATLABFile(*args, **kwargs) Overloaded function.   1. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, fil: str) -> None   2. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, fil: str, stdCount: float) -> None   3. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, fil: str, stdCount: float, styleLandmarks: str) -> None   4. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, fil: str, stdCount: float, styleLandmarks: str, stylePath: str) -> None   5. saveMapAndPath2DRepresentationAsMATLABFile(self: mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D, fil: str, stdCount: float, styleLandmarks: str, stylePath: str, styleRobot: str) -> None   Save the current state of the filter (robot pose & map) to a MATLAB  script which displays all the elements in 2D   C++: mrpt::slam::CRangeBearingKFSLAM2D::saveMapAndPath2DRepresentationAsMATLABFile(const std::string &, float, const std::string &, const std::string &, const std::string &) const --> void Data descriptors defined here: options Data and other attributes defined here: TDataAssocInfo = <class 'mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D.TDataAssocInfo'> Information for data-association:     getLastDataAssociation TOptions = <class 'mrpt.pymrpt.mrpt.slam.CRangeBearingKFSLAM2D.TOptions'> The options for the algorithm Methods inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t: OnNewLandmarkAddedToMap(...) OnNewLandmarkAddedToMap(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t, in_obsIdx: int, in_idxNewFeat: int) -> None   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::OnNewLandmarkAddedToMap(size_t, size_t) --> void OnNormalizeStateVector(...) OnNormalizeStateVector(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::OnNormalizeStateVector() --> void OnPostIteration(...) OnPostIteration(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::OnPostIteration() --> void getLandmarkCov(...) getLandmarkCov(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t, idx: int, feat_cov: mrpt.pymrpt.mrpt.math.CMatrixFixed_double_2UL_2UL_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::getLandmarkCov(size_t, class mrpt::math::CMatrixFixed<double, 2, 2> &) const --> void getLandmarkMean(...) getLandmarkMean(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t, idx: int, feat: mrpt.pymrpt.mrpt.math.CMatrixFixed_double_2UL_1UL_t) -> None   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::getLandmarkMean(size_t, class mrpt::math::CMatrixFixed<double, 2, 1> &) const --> void getNumberOfLandmarksInTheMap(...) getNumberOfLandmarksInTheMap(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::getNumberOfLandmarksInTheMap() const --> size_t getProfiler(...) getProfiler(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> mrpt.pymrpt.mrpt.system.CTimeLogger   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::getProfiler() --> class mrpt::system::CTimeLogger & getStateVectorLength(...) getStateVectorLength(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::getStateVectorLength() const --> size_t internal_getPkk(...) internal_getPkk(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> mrpt.pymrpt.mrpt.math.CMatrixDynamic_double_t   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::internal_getPkk() --> class mrpt::math::CMatrixDynamic<double> & internal_getXkk(...) internal_getXkk(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> mrpt::math::CVectorDynamic<double>   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::internal_getXkk() --> class mrpt::math::CVectorDynamic<double> & isMapEmpty(...) isMapEmpty(self: mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t) -> bool   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::isMapEmpty() const --> bool Static methods inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t: get_action_size(...) from builtins.PyCapsule get_action_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::get_action_size() --> size_t get_feature_size(...) from builtins.PyCapsule get_feature_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::get_feature_size() --> size_t get_observation_size(...) from builtins.PyCapsule get_observation_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::get_observation_size() --> size_t get_vehicle_size(...) from builtins.PyCapsule get_vehicle_size() -> int   C++: mrpt::bayes::CKalmanFilterCapable<3, 2, 2, 3>::get_vehicle_size() --> size_t Readonly properties inherited from mrpt.pymrpt.mrpt.bayes.CKalmanFilterCapable_3UL_2UL_2UL_3UL_double_t: KF_options Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class CRejectionSamplingRangeOnlyLocalization(mrpt.pymrpt.mrpt.bayes.CRejectionSamplingCapable_mrpt_poses_CPose2D_mrpt_bayes_particle_storage_mode_POINTER_t)     An implementation of rejection sampling for generating 2D robot pose from range-only measurements within a landmarks (beacons) map.    Before calling the method "rejectionSampling" to generate the samples, you must call "setParams".    It is assumed a planar scenario, where the robot is at a fixed height (default=0).     bayes::CRejectionSamplingCapable     Method resolution order: CRejectionSamplingRangeOnlyLocalization mrpt.pymrpt.mrpt.bayes.CRejectionSamplingCapable_mrpt_poses_CPose2D_mrpt_bayes_particle_storage_mode_POINTER_t pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, arg0: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, arg0: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, : mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization) -> mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization   C++: mrpt::slam::CRejectionSamplingRangeOnlyLocalization::operator=(const class mrpt::slam::CRejectionSamplingRangeOnlyLocalization &) --> class mrpt::slam::CRejectionSamplingRangeOnlyLocalization & setParams(...) setParams(*args, **kwargs) Overloaded function.   1. setParams(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, beaconsMap: mrpt.pymrpt.mrpt.maps.CLandmarksMap, observation: mrpt.pymrpt.mrpt.obs.CObservationBeaconRanges, sigmaRanges: float, oldPose: mrpt.pymrpt.mrpt.poses.CPose2D) -> bool   2. setParams(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, beaconsMap: mrpt.pymrpt.mrpt.maps.CLandmarksMap, observation: mrpt.pymrpt.mrpt.obs.CObservationBeaconRanges, sigmaRanges: float, oldPose: mrpt.pymrpt.mrpt.poses.CPose2D, robot_z: float) -> bool   3. setParams(self: mrpt.pymrpt.mrpt.slam.CRejectionSamplingRangeOnlyLocalization, beaconsMap: mrpt.pymrpt.mrpt.maps.CLandmarksMap, observation: mrpt.pymrpt.mrpt.obs.CObservationBeaconRanges, sigmaRanges: float, oldPose: mrpt.pymrpt.mrpt.poses.CPose2D, robot_z: float, autoCheckAngleRanges: bool) -> bool   The parameters used in the generation of random samples:      The map containing the N beacons (indexed by their  "beacon ID"s). Only the mean 3D position of the beacons is used, the  covariance is ignored.      An observation with, at least ONE range measurement.      The standard deviation of the "range measurement  noise".      The height of the robot on the floor (default=0). Note  that the beacon sensor on the robot may be at a different height,  according to data within the observation object.      Whether to make a simple check for potential  good angles from the beacons to generate samples (disable to speed-up the  preparation vs. making slower the drawn).   This method fills out the member "m_dataPerBeacon".      true if at least ONE beacon has been successfully loaded, false  otherwise. In this case do not call "rejectionSampling" or an exception  will be launch, since there is no information to generate samples.   C++: mrpt::slam::CRejectionSamplingRangeOnlyLocalization::setParams(const class mrpt::maps::CLandmarksMap &, const class mrpt::obs::CObservationBeaconRanges &, float, const class mrpt::poses::CPose2D &, float, bool) --> bool Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t(pybind11_builtins.pybind11_object)       Method resolution order: PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t pybind11_builtins.pybind11_object builtins.object Methods defined here: PF_SLAM_computeObservationLikelihoodForParticle(...) PF_SLAM_computeObservationLikelihoodForParticle(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, particleIndexForMap: int, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, x: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::PF_SLAM_computeObservationLikelihoodForParticle(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D &) const --> double PF_SLAM_implementation_custom_update_particle_with_new_pose(...) PF_SLAM_implementation_custom_update_particle_with_new_pose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, particleData: mrpt::maps::CRBPFParticleData, newPose: mrpt.pymrpt.mrpt.math.TPose3D) -> None   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::PF_SLAM_implementation_custom_update_particle_with_new_pose(class mrpt::maps::CRBPFParticleData *, const struct mrpt::math::TPose3D &) const --> void PF_SLAM_implementation_doWeHaveValidObservations(...) PF_SLAM_implementation_doWeHaveValidObservations(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, particles: List[mrpt::bayes::CProbabilityParticle<mrpt::maps::CRBPFParticleData, (mrpt::bayes::particle_storage_mode)1>], sf: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> bool   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::PF_SLAM_implementation_doWeHaveValidObservations(const class std::deque<struct mrpt::bayes::CProbabilityParticle<class mrpt::maps::CRBPFParticleData, mrpt::bayes::particle_storage_mode::POINTER> > &, const class mrpt::obs::CSensoryFrame *) const --> bool PF_SLAM_implementation_skipRobotMovement(...) PF_SLAM_implementation_skipRobotMovement(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t) -> bool   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::PF_SLAM_implementation_skipRobotMovement() const --> bool __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, arg0: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, : mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t) -> mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::operator=(const class mrpt::slam::PF_implementation<class mrpt::maps::CRBPFParticleData, class mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER> &) --> class mrpt::slam::PF_implementation<class mrpt::maps::CRBPFParticleData, class mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER> & getLastPose(...) getLastPose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_maps_CRBPFParticleData_mrpt_maps_CMultiMetricMapPDF_mrpt_bayes_particle_storage_mode_POINTER_t, i: int, is_valid_pose: bool) -> mrpt.pymrpt.mrpt.math.TPose3D   C++: mrpt::slam::PF_implementation<mrpt::maps::CRBPFParticleData, mrpt::maps::CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>::getLastPose(size_t, bool &) const --> struct mrpt::math::TPose3D Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t(pybind11_builtins.pybind11_object)       Method resolution order: PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t pybind11_builtins.pybind11_object builtins.object Methods defined here: PF_SLAM_computeObservationLikelihoodForParticle(...) PF_SLAM_computeObservationLikelihoodForParticle(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, particleIndexForMap: int, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, x: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_computeObservationLikelihoodForParticle(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D &) const --> double PF_SLAM_implementation_custom_update_particle_with_new_pose(...) PF_SLAM_implementation_custom_update_particle_with_new_pose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, particleData: mrpt.pymrpt.mrpt.math.TPose2D, newPose: mrpt.pymrpt.mrpt.math.TPose3D) -> None   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_custom_update_particle_with_new_pose(struct mrpt::math::TPose2D *, const struct mrpt::math::TPose3D &) const --> void PF_SLAM_implementation_doWeHaveValidObservations(...) PF_SLAM_implementation_doWeHaveValidObservations(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, particles: List[mrpt::bayes::CProbabilityParticle<mrpt::math::TPose2D, (mrpt::bayes::particle_storage_mode)0>], sf: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_doWeHaveValidObservations(const class std::deque<struct mrpt::bayes::CProbabilityParticle<struct mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE> > &, const class mrpt::obs::CSensoryFrame *) const --> bool PF_SLAM_implementation_skipRobotMovement(...) PF_SLAM_implementation_skipRobotMovement(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_skipRobotMovement() const --> bool __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, arg0: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, : mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t) -> mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::operator=(const class mrpt::slam::PF_implementation<struct mrpt::math::TPose2D, class mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE> &) --> class mrpt::slam::PF_implementation<struct mrpt::math::TPose2D, class mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE> & getLastPose(...) getLastPose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose2D_mrpt_slam_CMonteCarloLocalization2D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, is_valid_pose: bool) -> mrpt.pymrpt.mrpt.math.TPose3D   C++: mrpt::slam::PF_implementation<mrpt::math::TPose2D, mrpt::slam::CMonteCarloLocalization2D, mrpt::bayes::particle_storage_mode::VALUE>::getLastPose(size_t, bool &) const --> struct mrpt::math::TPose3D Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t(pybind11_builtins.pybind11_object)       Method resolution order: PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t pybind11_builtins.pybind11_object builtins.object Methods defined here: PF_SLAM_computeObservationLikelihoodForParticle(...) PF_SLAM_computeObservationLikelihoodForParticle(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, PF_options: mrpt.pymrpt.mrpt.bayes.CParticleFilter.TParticleFilterOptions, particleIndexForMap: int, observation: mrpt.pymrpt.mrpt.obs.CSensoryFrame, x: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_computeObservationLikelihoodForParticle(const struct mrpt::bayes::CParticleFilter::TParticleFilterOptions &, size_t, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D &) const --> double PF_SLAM_implementation_custom_update_particle_with_new_pose(...) PF_SLAM_implementation_custom_update_particle_with_new_pose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, particleData: mrpt.pymrpt.mrpt.math.TPose3D, newPose: mrpt.pymrpt.mrpt.math.TPose3D) -> None   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_custom_update_particle_with_new_pose(struct mrpt::math::TPose3D *, const struct mrpt::math::TPose3D &) const --> void PF_SLAM_implementation_doWeHaveValidObservations(...) PF_SLAM_implementation_doWeHaveValidObservations(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, particles: List[mrpt::bayes::CProbabilityParticle<mrpt::math::TPose3D, (mrpt::bayes::particle_storage_mode)0>], sf: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_doWeHaveValidObservations(const class std::deque<struct mrpt::bayes::CProbabilityParticle<struct mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE> > &, const class mrpt::obs::CSensoryFrame *) const --> bool PF_SLAM_implementation_skipRobotMovement(...) PF_SLAM_implementation_skipRobotMovement(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> bool   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::PF_SLAM_implementation_skipRobotMovement() const --> bool __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, arg0: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, : mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t) -> mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::operator=(const class mrpt::slam::PF_implementation<struct mrpt::math::TPose3D, class mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE> &) --> class mrpt::slam::PF_implementation<struct mrpt::math::TPose3D, class mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE> & getLastPose(...) getLastPose(self: mrpt.pymrpt.mrpt.slam.PF_implementation_mrpt_math_TPose3D_mrpt_slam_CMonteCarloLocalization3D_mrpt_bayes_particle_storage_mode_VALUE_t, i: int, is_valid_pose: bool) -> mrpt.pymrpt.mrpt.math.TPose3D   C++: mrpt::slam::PF_implementation<mrpt::math::TPose3D, mrpt::slam::CMonteCarloLocalization3D, mrpt::bayes::particle_storage_mode::VALUE>::getLastPose(size_t, bool &) const --> struct mrpt::math::TPose3D Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TDataAssociationMethod(pybind11_builtins.pybind11_object)       Method resolution order: TDataAssociationMethod pybind11_builtins.pybind11_object builtins.object Methods defined here: __and__(...) __and__(self: object, other: object) -> object __eq__(...) __eq__(self: object, other: object) -> bool __ge__(...) __ge__(self: object, other: object) -> bool __getstate__(...) __getstate__(self: object) -> int __gt__(...) __gt__(self: object, other: object) -> bool __hash__(...) __hash__(self: object) -> int __index__(...) __index__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMethod) -> int __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMethod, value: int) -> None __int__(...) __int__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMethod) -> int __invert__(...) __invert__(self: object) -> object __le__(...) __le__(self: object, other: object) -> bool __lt__(...) __lt__(self: object, other: object) -> bool __ne__(...) __ne__(self: object, other: object) -> bool __or__(...) __or__(self: object, other: object) -> object __rand__(...) __rand__(self: object, other: object) -> object __repr__(...) __repr__(self: object) -> str __ror__(...) __ror__(self: object, other: object) -> object __rxor__(...) __rxor__(self: object, other: object) -> object __setstate__(...) __setstate__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMethod, state: int) -> None __str__ = name(...) name(self: handle) -> str __xor__(...) __xor__(self: object, other: object) -> object Readonly properties defined here: __members__ name name(self: handle) -> str value Data and other attributes defined here: assocJCBB = <TDataAssociationMethod.assocJCBB: 1> assocNN = <TDataAssociationMethod.assocNN: 0> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TDataAssociationMetric(pybind11_builtins.pybind11_object)       Method resolution order: TDataAssociationMetric pybind11_builtins.pybind11_object builtins.object Methods defined here: __and__(...) __and__(self: object, other: object) -> object __eq__(...) __eq__(self: object, other: object) -> bool __ge__(...) __ge__(self: object, other: object) -> bool __getstate__(...) __getstate__(self: object) -> int __gt__(...) __gt__(self: object, other: object) -> bool __hash__(...) __hash__(self: object) -> int __index__(...) __index__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMetric) -> int __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMetric, value: int) -> None __int__(...) __int__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMetric) -> int __invert__(...) __invert__(self: object) -> object __le__(...) __le__(self: object, other: object) -> bool __lt__(...) __lt__(self: object, other: object) -> bool __ne__(...) __ne__(self: object, other: object) -> bool __or__(...) __or__(self: object, other: object) -> object __rand__(...) __rand__(self: object, other: object) -> object __repr__(...) __repr__(self: object) -> str __ror__(...) __ror__(self: object, other: object) -> object __rxor__(...) __rxor__(self: object, other: object) -> object __setstate__(...) __setstate__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationMetric, state: int) -> None __str__ = name(...) name(self: handle) -> str __xor__(...) __xor__(self: object, other: object) -> object Readonly properties defined here: __members__ name name(self: handle) -> str value Data and other attributes defined here: metricML = <TDataAssociationMetric.metricML: 1> metricMaha = <TDataAssociationMetric.metricMaha: 0> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TDataAssociationResults(pybind11_builtins.pybind11_object)     The results from mrpt::slam::data_association_independent_predictions()     Method resolution order: TDataAssociationResults pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationResults) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.TDataAssociationResults, arg0: mrpt.pymrpt.mrpt.slam.TDataAssociationResults) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.TDataAssociationResults, : mrpt.pymrpt.mrpt.slam.TDataAssociationResults) -> mrpt.pymrpt.mrpt.slam.TDataAssociationResults   C++: mrpt::slam::TDataAssociationResults::operator=(const struct mrpt::slam::TDataAssociationResults &) --> struct mrpt::slam::TDataAssociationResults & clear(...) clear(self: mrpt.pymrpt.mrpt.slam.TDataAssociationResults) -> None   C++: mrpt::slam::TDataAssociationResults::clear() --> void Data descriptors defined here: associations distance indiv_compatibility indiv_compatibility_counts indiv_distances nNodesExploredInJCBB Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TICPAlgorithm(pybind11_builtins.pybind11_object)       Method resolution order: TICPAlgorithm pybind11_builtins.pybind11_object builtins.object Methods defined here: __and__(...) __and__(self: object, other: object) -> object __eq__(...) __eq__(self: object, other: object) -> bool __ge__(...) __ge__(self: object, other: object) -> bool __getstate__(...) __getstate__(self: object) -> int __gt__(...) __gt__(self: object, other: object) -> bool __hash__(...) __hash__(self: object) -> int __index__(...) __index__(self: mrpt.pymrpt.mrpt.slam.TICPAlgorithm) -> int __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.TICPAlgorithm, value: int) -> None __int__(...) __int__(self: mrpt.pymrpt.mrpt.slam.TICPAlgorithm) -> int __invert__(...) __invert__(self: object) -> object __le__(...) __le__(self: object, other: object) -> bool __lt__(...) __lt__(self: object, other: object) -> bool __ne__(...) __ne__(self: object, other: object) -> bool __or__(...) __or__(self: object, other: object) -> object __rand__(...) __rand__(self: object, other: object) -> object __repr__(...) __repr__(self: object) -> str __ror__(...) __ror__(self: object, other: object) -> object __rxor__(...) __rxor__(self: object, other: object) -> object __setstate__(...) __setstate__(self: mrpt.pymrpt.mrpt.slam.TICPAlgorithm, state: int) -> None __str__ = name(...) name(self: handle) -> str __xor__(...) __xor__(self: object, other: object) -> object Readonly properties defined here: __members__ name name(self: handle) -> str value Data and other attributes defined here: icpClassic = <TICPAlgorithm.icpClassic: 0> icpLevenbergMarquardt = <TICPAlgorithm.icpLevenbergMarquardt: 1> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TICPCovarianceMethod(pybind11_builtins.pybind11_object)       Method resolution order: TICPCovarianceMethod pybind11_builtins.pybind11_object builtins.object Methods defined here: __and__(...) __and__(self: object, other: object) -> object __eq__(...) __eq__(self: object, other: object) -> bool __ge__(...) __ge__(self: object, other: object) -> bool __getstate__(...) __getstate__(self: object) -> int __gt__(...) __gt__(self: object, other: object) -> bool __hash__(...) __hash__(self: object) -> int __index__(...) __index__(self: mrpt.pymrpt.mrpt.slam.TICPCovarianceMethod) -> int __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.TICPCovarianceMethod, value: int) -> None __int__(...) __int__(self: mrpt.pymrpt.mrpt.slam.TICPCovarianceMethod) -> int __invert__(...) __invert__(self: object) -> object __le__(...) __le__(self: object, other: object) -> bool __lt__(...) __lt__(self: object, other: object) -> bool __ne__(...) __ne__(self: object, other: object) -> bool __or__(...) __or__(self: object, other: object) -> object __rand__(...) __rand__(self: object, other: object) -> object __repr__(...) __repr__(self: object) -> str __ror__(...) __ror__(self: object, other: object) -> object __rxor__(...) __rxor__(self: object, other: object) -> object __setstate__(...) __setstate__(self: mrpt.pymrpt.mrpt.slam.TICPCovarianceMethod, state: int) -> None __str__ = name(...) name(self: handle) -> str __xor__(...) __xor__(self: object, other: object) -> object Readonly properties defined here: __members__ name name(self: handle) -> str value Data and other attributes defined here: icpCovFiniteDifferences = <TICPCovarianceMethod.icpCovFiniteDifferences: 1> icpCovLinealMSE = <TICPCovarianceMethod.icpCovLinealMSE: 0> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TKLDParams(mrpt.pymrpt.mrpt.config.CLoadableOptions)     Option set for KLD algorithm.     Method resolution order: TKLDParams mrpt.pymrpt.mrpt.config.CLoadableOptions pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.TKLDParams) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.TKLDParams, arg0: mrpt.pymrpt.mrpt.slam.TKLDParams) -> None   3. __init__(self: mrpt.pymrpt.mrpt.slam.TKLDParams, arg0: mrpt.pymrpt.mrpt.slam.TKLDParams) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.TKLDParams, : mrpt.pymrpt.mrpt.slam.TKLDParams) -> mrpt.pymrpt.mrpt.slam.TKLDParams   C++: mrpt::slam::TKLDParams::operator=(const class mrpt::slam::TKLDParams &) --> class mrpt::slam::TKLDParams & loadFromConfigFile(...) loadFromConfigFile(self: mrpt.pymrpt.mrpt.slam.TKLDParams, source: mrpt.pymrpt.mrpt.config.CConfigFileBase, section: str) -> None   C++: mrpt::slam::TKLDParams::loadFromConfigFile(const class mrpt::config::CConfigFileBase &, const std::string &) --> void Data descriptors defined here: KLD_binSize_PHI KLD_binSize_XY KLD_delta KLD_epsilon KLD_maxSampleSize KLD_minSampleSize KLD_minSamplesPerBin Methods inherited from mrpt.pymrpt.mrpt.config.CLoadableOptions: dumpToConsole(...) dumpToConsole(self: mrpt.pymrpt.mrpt.config.CLoadableOptions) -> None   Just like  but sending the text to the console  (std::cout)    C++: mrpt::config::CLoadableOptions::dumpToConsole() const --> void loadFromConfigFileName(...) loadFromConfigFileName(self: mrpt.pymrpt.mrpt.config.CLoadableOptions, config_file: str, section: str) -> None   Behaves like loadFromConfigFile, but you can pass directly a file name  and a temporary CConfigFile object will be created automatically to load  the file.      loadFromConfigFile   C++: mrpt::config::CLoadableOptions::loadFromConfigFileName(const std::string &, const std::string &) --> void saveToConfigFile(...) saveToConfigFile(self: mrpt.pymrpt.mrpt.config.CLoadableOptions, target: mrpt::config::CConfigFileBase, section: str) -> None   This method saves the options to a ".ini"-like file or memory-stored  string list.      loadFromConfigFile, saveToConfigFileName   C++: mrpt::config::CLoadableOptions::saveToConfigFile(class mrpt::config::CConfigFileBase &, const std::string &) const --> void saveToConfigFileName(...) saveToConfigFileName(self: mrpt.pymrpt.mrpt.config.CLoadableOptions, config_file: str, section: str) -> None   Behaves like saveToConfigFile, but you can pass directly a file name and  a temporary CConfigFile object will be created automatically to save the  file.      saveToConfigFile, loadFromConfigFileName   C++: mrpt::config::CLoadableOptions::saveToConfigFileName(const std::string &, const std::string &) const --> void Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TMetricMapAlignmentResult(pybind11_builtins.pybind11_object)     Used as base class for other result structures of each particular algorithm in CMetricMapsAlignmentAlgorithm derived classes.     Method resolution order: TMetricMapAlignmentResult pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult, arg0: mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult, : mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult) -> mrpt.pymrpt.mrpt.slam.TMetricMapAlignmentResult   C++: mrpt::slam::TMetricMapAlignmentResult::operator=(const struct mrpt::slam::TMetricMapAlignmentResult &) --> struct mrpt::slam::TMetricMapAlignmentResult & Data descriptors defined here: executionTime Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class TMonteCarloLocalizationParams(pybind11_builtins.pybind11_object)     The struct for passing extra simulation parameters to the prediction stage when running a particle filter.     Method resolution order: TMonteCarloLocalizationParams pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams, arg0: mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams, : mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams) -> mrpt.pymrpt.mrpt.slam.TMonteCarloLocalizationParams   C++: mrpt::slam::TMonteCarloLocalizationParams::operator=(const struct mrpt::slam::TMonteCarloLocalizationParams &) --> struct mrpt::slam::TMonteCarloLocalizationParams & Data descriptors defined here: KLD_params metricMap metricMaps Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class map_keyframe_t(pybind11_builtins.pybind11_object)     Map keyframe, comprising raw observations and they as a metric map. For use in CIncrementalMapPartitioner     Method resolution order: map_keyframe_t pybind11_builtins.pybind11_object builtins.object Methods defined here: __init__(...) __init__(*args, **kwargs) Overloaded function.   1. __init__(self: mrpt.pymrpt.mrpt.slam.map_keyframe_t) -> None   2. __init__(self: mrpt.pymrpt.mrpt.slam.map_keyframe_t, arg0: mrpt.pymrpt.mrpt.slam.map_keyframe_t) -> None assign(...) assign(self: mrpt.pymrpt.mrpt.slam.map_keyframe_t, : mrpt.pymrpt.mrpt.slam.map_keyframe_t) -> mrpt.pymrpt.mrpt.slam.map_keyframe_t   C++: mrpt::slam::map_keyframe_t::operator=(const struct mrpt::slam::map_keyframe_t &) --> struct mrpt::slam::map_keyframe_t & Data descriptors defined here: kf_id metric_map raw_observations Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.   class similarity_method_t(pybind11_builtins.pybind11_object)       Method resolution order: similarity_method_t pybind11_builtins.pybind11_object builtins.object Methods defined here: __and__(...) __and__(self: object, other: object) -> object __eq__(...) __eq__(self: object, other: object) -> bool __ge__(...) __ge__(self: object, other: object) -> bool __getstate__(...) __getstate__(self: object) -> int __gt__(...) __gt__(self: object, other: object) -> bool __hash__(...) __hash__(self: object) -> int __index__(...) __index__(self: mrpt.pymrpt.mrpt.slam.similarity_method_t) -> int __init__(...) __init__(self: mrpt.pymrpt.mrpt.slam.similarity_method_t, value: int) -> None __int__(...) __int__(self: mrpt.pymrpt.mrpt.slam.similarity_method_t) -> int __invert__(...) __invert__(self: object) -> object __le__(...) __le__(self: object, other: object) -> bool __lt__(...) __lt__(self: object, other: object) -> bool __ne__(...) __ne__(self: object, other: object) -> bool __or__(...) __or__(self: object, other: object) -> object __rand__(...) __rand__(self: object, other: object) -> object __repr__(...) __repr__(self: object) -> str __ror__(...) __ror__(self: object, other: object) -> object __rxor__(...) __rxor__(self: object, other: object) -> object __setstate__(...) __setstate__(self: mrpt.pymrpt.mrpt.slam.similarity_method_t, state: int) -> None __str__ = name(...) name(self: handle) -> str __xor__(...) __xor__(self: object, other: object) -> object Readonly properties defined here: __members__ name name(self: handle) -> str value Data and other attributes defined here: smCUSTOM_FUNCTION = <similarity_method_t.smCUSTOM_FUNCTION: 2> smMETRIC_MAP_MATCHING = <similarity_method_t.smMETRIC_MAP_MATCHING: 0> smOBSERVATION_OVERLAP = <similarity_method_t.smOBSERVATION_OVERLAP: 1> Static methods inherited from pybind11_builtins.pybind11_object: __new__(*args, **kwargs) from pybind11_builtins.pybind11_type Create and return a new object.  See help(type) for accurate signature.

Functions
		observationsOverlap(...) method of builtins.PyCapsule instance observationsOverlap(*args, **kwargs) Overloaded function.   1. observationsOverlap(o1: mrpt.pymrpt.mrpt.obs.CObservation, o2: mrpt.pymrpt.mrpt.obs.CObservation) -> float   2. observationsOverlap(o1: mrpt.pymrpt.mrpt.obs.CObservation, o2: mrpt.pymrpt.mrpt.obs.CObservation, pose_o2_wrt_o1: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Estimates the "overlap" or "matching ratio" of two observations (range  [0,1]), possibly taking into account their relative positions.       This is used in mrpt::slam::CIncrementalMapPartitioner   C++: mrpt::slam::observationsOverlap(const class mrpt::obs::CObservation *, const class mrpt::obs::CObservation *, const class mrpt::poses::CPose3D *) --> double   3. observationsOverlap(o1: mrpt.pymrpt.mrpt.obs.CObservation, o2: mrpt.pymrpt.mrpt.obs.CObservation) -> float   4. observationsOverlap(o1: mrpt.pymrpt.mrpt.obs.CObservation, o2: mrpt.pymrpt.mrpt.obs.CObservation, pose_o2_wrt_o1: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Estimates the "overlap" or "matching ratio" of two observations (range  [0,1]), possibly taking into account their relative positions.       This is used in mrpt::slam::CIncrementalMapPartitioner   C++: mrpt::slam::observationsOverlap(const class std::shared_ptr<class mrpt::obs::CObservation> &, const class std::shared_ptr<class mrpt::obs::CObservation> &, const class mrpt::poses::CPose3D *) --> double   5. observationsOverlap(sf1: mrpt.pymrpt.mrpt.obs.CSensoryFrame, sf2: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> float   6. observationsOverlap(sf1: mrpt.pymrpt.mrpt.obs.CSensoryFrame, sf2: mrpt.pymrpt.mrpt.obs.CSensoryFrame, pose_sf2_wrt_sf1: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Estimates the "overlap" or "matching ratio" of two set of observations  (range [0,1]), possibly taking into account their relative positions.    This method computes the average between each of the observations in each  SF.       This is used in mrpt::slam::CIncrementalMapPartitioner   C++: mrpt::slam::observationsOverlap(const class mrpt::obs::CSensoryFrame &, const class mrpt::obs::CSensoryFrame &, const class mrpt::poses::CPose3D *) --> double   7. observationsOverlap(sf1: mrpt.pymrpt.mrpt.obs.CSensoryFrame, sf2: mrpt.pymrpt.mrpt.obs.CSensoryFrame) -> float   8. observationsOverlap(sf1: mrpt.pymrpt.mrpt.obs.CSensoryFrame, sf2: mrpt.pymrpt.mrpt.obs.CSensoryFrame, pose_sf2_wrt_sf1: mrpt.pymrpt.mrpt.poses.CPose3D) -> float   Estimates the "overlap" or "matching ratio" of two set of observations  (range [0,1]), possibly taking into account their relative positions.    This method computes the average between each of the observations in each  SF.       This is used in mrpt::slam::CIncrementalMapPartitioner   C++: mrpt::slam::observationsOverlap(const class std::shared_ptr<class mrpt::obs::CSensoryFrame> &, const class std::shared_ptr<class mrpt::obs::CSensoryFrame> &, const class mrpt::poses::CPose3D *) --> double

Data
		assocJCBB = <TDataAssociationMethod.assocJCBB: 1> assocNN = <TDataAssociationMethod.assocNN: 0> icpClassic = <TICPAlgorithm.icpClassic: 0> icpCovFiniteDifferences = <TICPCovarianceMethod.icpCovFiniteDifferences: 1> icpCovLinealMSE = <TICPCovarianceMethod.icpCovLinealMSE: 0> icpLevenbergMarquardt = <TICPAlgorithm.icpLevenbergMarquardt: 1> metricML = <TDataAssociationMetric.metricML: 1> metricMaha = <TDataAssociationMetric.metricMaha: 0> smCUSTOM_FUNCTION = <similarity_method_t.smCUSTOM_FUNCTION: 2> smMETRIC_MAP_MATCHING = <similarity_method_t.smMETRIC_MAP_MATCHING: 0> smOBSERVATION_OVERLAP = <similarity_method_t.smOBSERVATION_OVERLAP: 1>