Detailed Description

An implementation of EKF-based SLAM with range-bearing sensors, odometry, a full 6D robot pose, and 3D landmarks.

The main method is "processActionObservation" which processes pairs of action/observation. The state vector comprises: 3D robot position, a quaternion for its attitude, and the 3D landmarks in the map.

The following Wiki page describes an front-end application based on this class: http://www.mrpt.org/Application:kf-slam

For the theory behind this implementation, see the technical report in: http://www.mrpt.org/6D-SLAM

See also: An implementation for 2D only: CRangeBearingKFSLAM2D

Definition at line 52 of file CRangeBearingKFSLAM.h.

#include <mrpt/slam/CRangeBearingKFSLAM.h>

Inheritance diagram for mrpt::slam::CRangeBearingKFSLAM:

Classes
struct	TDataAssocInfo
	Information for data-association: More...

struct	TOptions
	The options for the algorithm. More...

Public Types
typedef mrpt::math::TPoint3D	landmark_point_t
	Either mrpt::math::TPoint2D or mrpt::math::TPoint3D. More...

typedef double	kftype
	The numeric type used in the Kalman Filter (default=double) More...

typedef CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >	KFCLASS
	My class, in a shorter name! More...

typedef Eigen::Matrix< double, Eigen::Dynamic, 1 >	KFVector

typedef mrpt::math::CMatrixTemplateNumeric< double >	KFMatrix

typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, VEH_SIZE >	KFMatrix_VxV

typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, OBS_SIZE >	KFMatrix_OxO

typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, FEAT_SIZE >	KFMatrix_FxF

typedef mrpt::math::CMatrixFixedNumeric< double, ACT_SIZE, ACT_SIZE >	KFMatrix_AxA

typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, OBS_SIZE >	KFMatrix_VxO

typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, FEAT_SIZE >	KFMatrix_VxF

typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, VEH_SIZE >	KFMatrix_FxV

typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, OBS_SIZE >	KFMatrix_FxO

typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, FEAT_SIZE >	KFMatrix_OxF

typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, VEH_SIZE >	KFMatrix_OxV

typedef mrpt::math::CArrayNumeric< double, VEH_SIZE >	KFArray_VEH

typedef mrpt::math::CArrayNumeric< double, ACT_SIZE >	KFArray_ACT

typedef mrpt::math::CArrayNumeric< double, OBS_SIZE >	KFArray_OBS

typedef mrpt::aligned_containers< KFArray_OBS >::vector_t	vector_KFArray_OBS

typedef mrpt::math::CArrayNumeric< double, FEAT_SIZE >	KFArray_FEAT

Public Member Functions
	CRangeBearingKFSLAM ()
	Constructor. More...

virtual	~CRangeBearingKFSLAM ()
	Destructor: More...

void	reset ()
	Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0). More...

void	processActionObservation (mrpt::obs::CActionCollection::Ptr &action, mrpt::obs::CSensoryFrame::Ptr &SF)
	Process one new action and observations to update the map and robot pose estimate. More...

void	getCurrentState (mrpt::poses::CPose3DQuatPDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint3D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const
	Returns the complete mean and cov. More...

void	getCurrentState (mrpt::poses::CPose3DPDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint3D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const
	Returns the complete mean and cov. More...

void	getCurrentRobotPose (mrpt::poses::CPose3DQuatPDFGaussian &out_robotPose) const
	Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with rotation as a quaternion). More...

mrpt::poses::CPose3DQuat	getCurrentRobotPoseMean () const
	Get the current robot pose mean, as a 3D+quaternion pose. More...

void	getCurrentRobotPose (mrpt::poses::CPose3DPDFGaussian &out_robotPose) const
	Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with rotation as 3 angles). More...

void	getAs3DObject (mrpt::opengl::CSetOfObjects::Ptr &outObj) const
	Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state. More...

void	loadOptions (const mrpt::utils::CConfigFileBase &ini)
	Load options from a ini-like file/text. More...

const TDataAssocInfo &	getLastDataAssociation () const
	Returns a read-only reference to the information on the last data-association. More...

void	getLastPartition (std::vector< vector_uint > &parts)
	Return the last partition of the sequence of sensoryframes (it is NOT a partition of the map!!) Only if options.doPartitioningExperiment = true. More...

void	getLastPartitionLandmarks (std::vector< vector_uint > &landmarksMembership) const
	Return the partitioning of the landmarks in clusters accoring to the last partition. More...

void	getLastPartitionLandmarksAsIfFixedSubmaps (size_t K, std::vector< vector_uint > &landmarksMembership)
	For testing only: returns the partitioning as "getLastPartitionLandmarks" but as if a fixed-size submaps (size K) were have been used. More...

double	computeOffDiagonalBlocksApproximationError (const std::vector< vector_uint > &landmarksMembership) const
	Computes the ratio of the missing information matrix elements which are ignored under a certain partitioning of the landmarks. More...

void	reconsiderPartitionsNow ()
	The partitioning of the entire map is recomputed again. More...

CIncrementalMapPartitioner::TOptions *	mapPartitionOptions ()
	Provides access to the parameters of the map partitioning algorithm. More...

void	saveMapAndPath2DRepresentationAsMATLABFile (const std::string &fil, float stdCount=3.0f, const std::string &styleLandmarks=std::string("b"), const std::string &stylePath=std::string("r"), const std::string &styleRobot=std::string("r")) const
	Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the elements in 2D. More...

size_t	getNumberOfLandmarksInTheMap () const

bool	isMapEmpty () const

size_t	getStateVectorLength () const

KFVector &	internal_getXkk ()

KFMatrix &	internal_getPkk ()

void	getLandmarkMean (size_t idx, KFArray_FEAT &feat) const
	Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems). More...

void	getLandmarkCov (size_t idx, KFMatrix_FxF &feat_cov) const
	Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems). More...

mrpt::utils::CTimeLogger &	getProfiler ()

Static Public Member Functions
static size_t	get_vehicle_size ()

static size_t	get_observation_size ()

static size_t	get_feature_size ()

static size_t	get_action_size ()

Public Attributes
mrpt::slam::CRangeBearingKFSLAM::TOptions	options

TKF_options	KF_options
	Generic options for the Kalman Filter algorithm itself. More...

Protected Member Functions
mrpt::poses::CPose3DQuat	getIncrementFromOdometry () const
	Return the last odometry, as a pose increment. More...

void	runOneKalmanIteration ()
	The main entry point, executes one complete step: prediction + update. More...

Virtual methods for Kalman Filter implementation
void	OnGetAction (KFArray_ACT &out_u) const
	Must return the action vector u. More...

void	OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const
	Implements the transition model $\hat{x}_{k\|k-1} = f( \hat{x}_{k-1\|k-1}, u_k )$ . More...

void	OnTransitionJacobian (KFMatrix_VxV &out_F) const
	Implements the transition Jacobian $\frac{\partial f}{\partial x}$ . More...

void	OnTransitionNoise (KFMatrix_VxV &out_Q) const
	Implements the transition noise covariance . More...

void	OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)
	This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map. More...

void	OnObservationModel (const vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const

void	OnObservationJacobians (const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const
	Implements the observation Jacobians $\frac{\partial h_i}{\partial x}$ and (when applicable) $\frac{\partial h_i}{\partial y_i}$ . More...

void	OnSubstractObservationVectors (KFArray_OBS &A, const KFArray_OBS &B) const
	Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles). More...

void	OnGetObservationNoise (KFMatrix_OxO &out_R) const
	Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. More...

void	OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, vector_size_t &out_LM_indices_to_predict) const
	This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. More...

void	OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const
	If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...

void	OnNewLandmarkAddedToMap (const size_t in_obsIdx, const size_t in_idxNewFeat)
	If applicable to the given problem, do here any special handling of adding a new landmark to the map. More...

void	OnNormalizeStateVector ()
	This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it. More...

Protected Attributes
mrpt::obs::CActionCollection::Ptr	m_action
	Set up by processActionObservation. More...

mrpt::obs::CSensoryFrame::Ptr	m_SF
	Set up by processActionObservation. More...

mrpt::utils::bimap< mrpt::maps::CLandmark::TLandmarkID, unsigned int >	m_IDs
	The mapping between landmark IDs and indexes in the Pkk cov. More...

CIncrementalMapPartitioner	mapPartitioner
	Used for map partitioning experiments. More...

mrpt::maps::CSimpleMap	m_SFs
	The sequence of all the observations and the robot path (kept for debugging, statistics,etc) More...

std::vector< vector_uint >	m_lastPartitionSet

TDataAssocInfo	m_last_data_association
	Last data association. More...

mrpt::utils::CTimeLogger	m_timLogger

Kalman filter state
KFVector	m_xkk
	The system state vector. More...

KFMatrix	m_pkk
	The system full covariance matrix. More...

Static Private Member Functions
static void	KF_aux_estimate_trans_jacobian (const KFArray_VEH &x, const std::pair< KFCLASS *, KFArray_ACT > &dat, KFArray_VEH &out_x)
	Auxiliary functions for Jacobian numeric estimation. More...

static void	KF_aux_estimate_obs_Hx_jacobian (const KFArray_VEH &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)

static void	KF_aux_estimate_obs_Hy_jacobian (const KFArray_FEAT &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)

Private Attributes
vector_KFArray_OBS	all_predictions

vector_size_t	predictLMidxs

mrpt::aligned_containers< KFMatrix_OxV >::vector_t	Hxs
	The vector of all partial Jacobians dh[i]_dx for each prediction. More...

mrpt::aligned_containers< KFMatrix_OxF >::vector_t	Hys
	The vector of all partial Jacobians dh[i]_dy[i] for each prediction. More...

KFMatrix	S

KFMatrix	Pkk_subset

vector_KFArray_OBS	Z

KFMatrix	K

KFMatrix	S_1

KFMatrix	dh_dx_full_obs

KFMatrix	aux_K_dh_dx

bool	m_user_didnt_implement_jacobian

Virtual methods for Kalman Filter implementation
virtual void	OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const
	If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...

virtual void	OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn, KFMatrix_FxF &out_dyn_dhn_R_dyn_dhnT, bool &out_use_dyn_dhn_jacobian) const
	If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...

virtual void	OnPostIteration ()
	This method is called after finishing one KF iteration and before returning from runOneKalmanIteration(). More...

virtual void	OnGetAction (KFArray_ACT &out_u) const=0
	Must return the action vector u. More...

virtual void	OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const=0
	Implements the transition model $\hat{x}_{k\|k-1} = f( \hat{x}_{k-1\|k-1}, u_k )$ . More...

virtual void	OnTransitionJacobian (KFMatrix_VxV &out_F) const
	Implements the transition Jacobian $\frac{\partial f}{\partial x}$ . More...

virtual void	OnTransitionJacobianNumericGetIncrements (KFArray_VEH &out_increments) const
	Only called if using a numeric approximation of the transition Jacobian, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian. More...

virtual void	OnTransitionNoise (KFMatrix_VxV &out_Q) const=0
	Implements the transition noise covariance . More...

virtual void	OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, mrpt::vector_size_t &out_LM_indices_to_predict) const
	This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. More...

virtual void	OnGetObservationNoise (KFMatrix_OxO &out_R) const=0
	Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. More...

virtual void	OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, mrpt::vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)=0
	This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map. More...

virtual void	OnObservationModel (const mrpt::vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const=0
	Implements the observation prediction . More...

virtual void	OnObservationJacobians (const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const
	Implements the observation Jacobians $\frac{\partial h_i}{\partial x}$ and (when applicable) $\frac{\partial h_i}{\partial y_i}$ . More...

virtual void	OnObservationJacobiansNumericGetIncrements (KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const
	Only called if using a numeric approximation of the observation Jacobians, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian. More...

virtual void	OnSubstractObservationVectors (KFArray_OBS &A, const KFArray_OBS &B) const
	Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles). More...

Member Typedef Documentation

◆ KFArray_ACT

typedef mrpt::math::CArrayNumeric<double , ACT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFArray_ACT

inherited

Definition at line 272 of file CKalmanFilterCapable.h.

◆ KFArray_FEAT

typedef mrpt::math::CArrayNumeric<double , FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFArray_FEAT

inherited

Definition at line 276 of file CKalmanFilterCapable.h.

◆ KFArray_OBS

typedef mrpt::math::CArrayNumeric<double , OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFArray_OBS

inherited

Definition at line 273 of file CKalmanFilterCapable.h.

◆ KFArray_VEH

typedef mrpt::math::CArrayNumeric<double , VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFArray_VEH

inherited

Definition at line 271 of file CKalmanFilterCapable.h.

◆ KFCLASS

typedef CKalmanFilterCapable<VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double > mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFCLASS

inherited

My class, in a shorter name!

Definition at line 241 of file CKalmanFilterCapable.h.

◆ KFMatrix

typedef mrpt::math::CMatrixTemplateNumeric<double > mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix

inherited

Definition at line 245 of file CKalmanFilterCapable.h.

◆ KFMatrix_AxA

typedef mrpt::math::CMatrixFixedNumeric<double , ACT_SIZE, ACT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_AxA

inherited

Definition at line 254 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxF

typedef mrpt::math::CMatrixFixedNumeric<double , FEAT_SIZE, FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_FxF

inherited

Definition at line 252 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxO

typedef mrpt::math::CMatrixFixedNumeric<double , FEAT_SIZE, OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_FxO

inherited

Definition at line 264 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxV

typedef mrpt::math::CMatrixFixedNumeric<double , FEAT_SIZE, VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_FxV

inherited

Definition at line 262 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxF

typedef mrpt::math::CMatrixFixedNumeric<double , OBS_SIZE, FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_OxF

inherited

Definition at line 267 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxO

typedef mrpt::math::CMatrixFixedNumeric<double , OBS_SIZE, OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_OxO

inherited

Definition at line 250 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxV

typedef mrpt::math::CMatrixFixedNumeric<double , OBS_SIZE, VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_OxV

inherited

Definition at line 269 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxF

typedef mrpt::math::CMatrixFixedNumeric<double , VEH_SIZE, FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_VxF

inherited

Definition at line 259 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxO

typedef mrpt::math::CMatrixFixedNumeric<double , VEH_SIZE, OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_VxO

inherited

Definition at line 257 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxV

typedef mrpt::math::CMatrixFixedNumeric<double , VEH_SIZE, VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFMatrix_VxV

inherited

Definition at line 248 of file CKalmanFilterCapable.h.

◆ kftype

typedef double mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::kftype

inherited

The numeric type used in the Kalman Filter (default=double)

Definition at line 237 of file CKalmanFilterCapable.h.

◆ KFVector

typedef Eigen::Matrix<double , Eigen::Dynamic, 1> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KFVector

inherited

Definition at line 244 of file CKalmanFilterCapable.h.

◆ landmark_point_t

typedef mrpt::math::TPoint3D mrpt::slam::CRangeBearingKFSLAM::landmark_point_t

Either mrpt::math::TPoint2D or mrpt::math::TPoint3D.

Definition at line 61 of file CRangeBearingKFSLAM.h.

◆ vector_KFArray_OBS

typedef mrpt::aligned_containers<KFArray_OBS>::vector_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::vector_KFArray_OBS

inherited

Definition at line 275 of file CKalmanFilterCapable.h.

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM()

CRangeBearingKFSLAM::CRangeBearingKFSLAM ( )

Constructor.

Definition at line 46 of file CRangeBearingKFSLAM.cpp.

References reset().

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◆ ~CRangeBearingKFSLAM()

CRangeBearingKFSLAM::~CRangeBearingKFSLAM ( )

virtual

Destructor:

Definition at line 88 of file CRangeBearingKFSLAM.cpp.

Member Function Documentation

◆ computeOffDiagonalBlocksApproximationError()

double CRangeBearingKFSLAM::computeOffDiagonalBlocksApproximationError ( const std::vector< vector_uint > & landmarksMembership ) const

Computes the ratio of the missing information matrix elements which are ignored under a certain partitioning of the landmarks.

See also: getLastPartitionLandmarks, getLastPartitionLandmarksAsIfFixedSubmaps

Definition at line 1197 of file CRangeBearingKFSLAM.cpp.

References ASSERT_, mrpt::math::countCommonElements(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, MRPT_END, and MRPT_START.

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◆ get_action_size()

static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::get_action_size ( )

inlinestaticinherited

Definition at line 230 of file CKalmanFilterCapable.h.

◆ get_feature_size()

static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::get_feature_size ( )

inlinestaticinherited

Definition at line 229 of file CKalmanFilterCapable.h.

◆ get_observation_size()

static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::get_observation_size ( )

inlinestaticinherited

Definition at line 228 of file CKalmanFilterCapable.h.

◆ get_vehicle_size()

static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::get_vehicle_size ( )

inlinestaticinherited

Definition at line 227 of file CKalmanFilterCapable.h.

◆ getAs3DObject()

void CRangeBearingKFSLAM::getAs3DObject ( mrpt::opengl::CSetOfObjects::Ptr & outObj ) const

Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state.

Parameters

out_objects

Definition at line 988 of file CRangeBearingKFSLAM.cpp.

References mrpt::opengl::stock_objects::CornerXYZ(), mrpt::poses::CPointPDFGaussian::cov, mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::format(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::utils::bimap< KEY, VALUE >::inverse(), m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPointPDFGaussian::mean, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::quantiles_3D_representation, mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

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◆ getCurrentRobotPose() [1/2]

void mrpt::slam::CRangeBearingKFSLAM::getCurrentRobotPose ( mrpt::poses::CPose3DPDFGaussian & out_robotPose ) const

inline

Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with rotation as 3 angles).

See also: getCurrentState

Definition at line 147 of file CRangeBearingKFSLAM.h.

References getCurrentRobotPose(), and mrpt::math::UNINITIALIZED_QUATERNION.

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◆ getCurrentRobotPose() [2/2]

void CRangeBearingKFSLAM::getCurrentRobotPose ( mrpt::poses::CPose3DQuatPDFGaussian & out_robotPose ) const

Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with rotation as a quaternion).

See also: getCurrentState, getCurrentRobotPoseMean

Definition at line 92 of file CRangeBearingKFSLAM.cpp.

References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::poses::CPose3DQuat::m_coords, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPose3DQuatPDFGaussian::mean, MRPT_END, and MRPT_START.

Referenced by getCurrentRobotPose(), and processActionObservation().

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◆ getCurrentRobotPoseMean()

mrpt::poses::CPose3DQuat CRangeBearingKFSLAM::getCurrentRobotPoseMean ( ) const

Get the current robot pose mean, as a 3D+quaternion pose.

See also: getCurrentRobotPose

Definition at line 117 of file CRangeBearingKFSLAM.cpp.

References ASSERTDEB_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, and mrpt::math::UNINITIALIZED_QUATERNION.

Referenced by OnInverseObservationModel(), OnObservationJacobians(), OnObservationModel(), OnTransitionJacobian(), OnTransitionModel(), and OnTransitionNoise().

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◆ getCurrentState() [1/2]

void mrpt::slam::CRangeBearingKFSLAM::getCurrentState	(	mrpt::poses::CPose3DPDFGaussian &	out_robotPose,
		std::vector< mrpt::math::TPoint3D > &	out_landmarksPositions,
		std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &	out_landmarkIDs,
		mrpt::math::CVectorDouble &	out_fullState,
		mrpt::math::CMatrixDouble &	out_fullCovariance
	)		const

inline

Returns the complete mean and cov.

Parameters

out_robotPose	The mean and the 7x7 covariance matrix of the robot 6D pose
out_landmarksPositions	One entry for each of the M landmark positions (3D).
out_landmarkIDs	Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
out_fullState	The complete state vector (7+3M).
out_fullCovariance	The full (7+3M)x(7+3M) covariance matrix of the filter.

See also: getCurrentRobotPose

Definition at line 115 of file CRangeBearingKFSLAM.h.

References getCurrentState(), and mrpt::math::UNINITIALIZED_QUATERNION.

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◆ getCurrentState() [2/2]

void CRangeBearingKFSLAM::getCurrentState	(	mrpt::poses::CPose3DQuatPDFGaussian &	out_robotPose,
		std::vector< mrpt::math::TPoint3D > &	out_landmarksPositions,
		std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &	out_landmarkIDs,
		mrpt::math::CVectorDouble &	out_fullState,
		mrpt::math::CMatrixDouble &	out_fullCovariance
	)		const

Returns the complete mean and cov.

Parameters

out_robotPose	The mean and the 7x7 covariance matrix of the robot 6D pose
out_landmarksPositions	One entry for each of the M landmark positions (3D).
out_landmarkIDs	Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
out_fullState	The complete state vector (7+3M).
out_fullCovariance	The full (7+3M)x(7+3M) covariance matrix of the filter.

See also: getCurrentRobotPose

Definition at line 136 of file CRangeBearingKFSLAM.cpp.

References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::utils::bimap< KEY, VALUE >::getInverseMap(), mrpt::poses::CPose3DQuat::m_coords, m_IDs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPose3DQuatPDFGaussian::mean, MRPT_END, and MRPT_START.

Referenced by getCurrentState().

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◆ getIncrementFromOdometry()

CPose3DQuat CRangeBearingKFSLAM::getIncrementFromOdometry ( ) const

protected

Return the last odometry, as a pose increment.

Definition at line 268 of file CRangeBearingKFSLAM.cpp.

References mrpt::slam::CRangeBearingKFSLAM::TOptions::force_ignore_odometry, m_action, and options.

Referenced by OnGetAction(), and OnTransitionJacobian().

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◆ getLandmarkCov()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getLandmarkCov	(	size_t	idx,
		KFMatrix_FxF &	feat_cov
	)		const

inlineinherited

Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems).

Exceptions

std::exception On idx>= getNumberOfLandmarksInTheMap()

Definition at line 296 of file CKalmanFilterCapable.h.

◆ getLandmarkMean()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getLandmarkMean	(	size_t	idx,
		KFArray_FEAT &	feat
	)		const

inlineinherited

Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems).

Exceptions

std::exception On idx>= getNumberOfLandmarksInTheMap()

Definition at line 285 of file CKalmanFilterCapable.h.

◆ getLastDataAssociation()

const TDataAssocInfo& mrpt::slam::CRangeBearingKFSLAM::getLastDataAssociation ( ) const

inline

Returns a read-only reference to the information on the last data-association.

Definition at line 256 of file CRangeBearingKFSLAM.h.

References m_last_data_association.

◆ getLastPartition()

void mrpt::slam::CRangeBearingKFSLAM::getLastPartition ( std::vector< vector_uint > & parts )

inline

Return the last partition of the sequence of sensoryframes (it is NOT a partition of the map!!) Only if options.doPartitioningExperiment = true.

See also: getLastPartitionLandmarks

Definition at line 266 of file CRangeBearingKFSLAM.h.

References m_lastPartitionSet.

◆ getLastPartitionLandmarks()

void CRangeBearingKFSLAM::getLastPartitionLandmarks ( std::vector< vector_uint > & landmarksMembership ) const

Return the partitioning of the landmarks in clusters accoring to the last partition.

Note that the same landmark may appear in different clusters (the partition is not in the space of landmarks) Only if options.doPartitioningExperiment = true

Parameters

landmarksMembership The i'th element of this vector is the set of clusters to which the i'th landmark in the map belongs to (landmark index != landmark ID !!).

See also: getLastPartition

Definition at line 1140 of file CRangeBearingKFSLAM.cpp.

References mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::utils::bimap< KEY, VALUE >::inverse(), m_IDs, m_lastPartitionSet, m_SFs, and options.

Referenced by getLastPartitionLandmarksAsIfFixedSubmaps().

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◆ getLastPartitionLandmarksAsIfFixedSubmaps()

void CRangeBearingKFSLAM::getLastPartitionLandmarksAsIfFixedSubmaps	(	size_t	K,
		std::vector< vector_uint > &	landmarksMembership
	)

For testing only: returns the partitioning as "getLastPartitionLandmarks" but as if a fixed-size submaps (size K) were have been used.

Definition at line 1105 of file CRangeBearingKFSLAM.cpp.

References getLastPartitionLandmarks(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::K, m_lastPartitionSet, m_SFs, and mrpt::maps::CSimpleMap::size().

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◆ getNumberOfLandmarksInTheMap()

size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getNumberOfLandmarksInTheMap ( ) const

inlineinherited

Definition at line 231 of file CKalmanFilterCapable.h.

◆ getProfiler()

mrpt::utils::CTimeLogger& mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getProfiler ( )

inlineinherited

Definition at line 620 of file CKalmanFilterCapable.h.

◆ getStateVectorLength()

size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getStateVectorLength ( ) const

inlineinherited

Definition at line 278 of file CKalmanFilterCapable.h.

◆ internal_getPkk()

KFMatrix& mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::internal_getPkk ( )

inlineinherited

Definition at line 280 of file CKalmanFilterCapable.h.

◆ internal_getXkk()

KFVector& mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::internal_getXkk ( )

inlineinherited

Definition at line 279 of file CKalmanFilterCapable.h.

◆ isMapEmpty()

bool mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::isMapEmpty ( ) const

inlineinherited

Definition at line 235 of file CKalmanFilterCapable.h.

◆ KF_aux_estimate_obs_Hx_jacobian()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KF_aux_estimate_obs_Hx_jacobian	(	const KFArray_VEH &	x,
		const std::pair< KFCLASS *, size_t > &	dat,
		KFArray_OBS &	out_x
	)

staticprivateinherited

Definition at line 658 of file CKalmanFilterCapable_impl.h.

◆ KF_aux_estimate_obs_Hy_jacobian()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KF_aux_estimate_obs_Hy_jacobian	(	const KFArray_FEAT &	x,
		const std::pair< KFCLASS *, size_t > &	dat,
		KFArray_OBS &	out_x
	)

staticprivateinherited

Definition at line 661 of file CKalmanFilterCapable_impl.h.

◆ KF_aux_estimate_trans_jacobian()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KF_aux_estimate_trans_jacobian	(	const KFArray_VEH &	x,
		const std::pair< KFCLASS *, KFArray_ACT > &	dat,
		KFArray_VEH &	out_x
	)

staticprivateinherited

Auxiliary functions for Jacobian numeric estimation.

Definition at line 655 of file CKalmanFilterCapable_impl.h.

◆ loadOptions()

void CRangeBearingKFSLAM::loadOptions ( const mrpt::utils::CConfigFileBase & ini )

Load options from a ini-like file/text.

Definition at line 741 of file CRangeBearingKFSLAM.cpp.

References mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::KF_options, mrpt::bayes::TKF_options::loadFromConfigFile(), mrpt::slam::CIncrementalMapPartitioner::TOptions::loadFromConfigFile(), mrpt::slam::CRangeBearingKFSLAM::TOptions::loadFromConfigFile(), mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::options, and options.

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◆ mapPartitionOptions()

CIncrementalMapPartitioner::TOptions* mrpt::slam::CRangeBearingKFSLAM::mapPartitionOptions ( )

inline

Provides access to the parameters of the map partitioning algorithm.

Definition at line 309 of file CRangeBearingKFSLAM.h.

References mapPartitioner, and mrpt::slam::CIncrementalMapPartitioner::options.

◆ OnGetAction() [1/2]

void CRangeBearingKFSLAM::OnGetAction ( KFArray_ACT & u ) const

protected

Must return the action vector u.

Parameters

out_u The action vector which will be passed to OnTransitionModel

Definition at line 293 of file CRangeBearingKFSLAM.cpp.

References getIncrementFromOdometry().

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◆ OnGetAction() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnGetAction ( KFArray_ACT & out_u ) const

protectedpure virtualinherited

Must return the action vector u.

Parameters

out_u The action vector which will be passed to OnTransitionModel

◆ OnGetObservationNoise() [1/2]

void CRangeBearingKFSLAM::OnGetObservationNoise ( KFMatrix_OxO & out_R ) const

protected

Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.

Parameters

out_R The noise covariance matrix. It might be non diagonal, but it'll usually be.

Definition at line 1368 of file CRangeBearingKFSLAM.cpp.

References options, mrpt::math::square(), mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_pitch, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_yaw.

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◆ OnGetObservationNoise() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnGetObservationNoise ( KFMatrix_OxO & out_R ) const

protectedpure virtualinherited

Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.

Parameters

out_R The noise covariance matrix. It might be non diagonal, but it'll usually be.

Note: Upon call, it can be assumed that the previous contents of out_R are all zeros.

◆ OnGetObservationsAndDataAssociation() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnGetObservationsAndDataAssociation	(	vector_KFArray_OBS &	out_z,
		mrpt::vector_int &	out_data_association,
		const vector_KFArray_OBS &	in_all_predictions,
		const KFMatrix &	in_S,
		const vector_size_t &	in_lm_indices_in_S,
		const KFMatrix_OxO &	in_R
	)

protectedpure virtualinherited

This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map.

Parameters

out_z	N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable.
out_data_association	An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration.
in_all_predictions	A vector with the prediction of ALL the landmarks in the map. Note that, in contrast, in_S only comprises a subset of all the landmarks.
in_S	The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a MO x MO matrix with M=length of "in_lm_indices_in_S".
in_lm_indices_in_S	The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.

This method will be called just once for each complete KF iteration.

Note: It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.

◆ OnGetObservationsAndDataAssociation() [2/2]

void CRangeBearingKFSLAM::OnGetObservationsAndDataAssociation	(	vector_KFArray_OBS &	Z,
		vector_int &	data_association,
		const vector_KFArray_OBS &	all_predictions,
		const KFMatrix &	S,
		const vector_size_t &	lm_indices_in_S,
		const KFMatrix_OxO &	R
	)

protected

This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map.

Parameters

out_z	N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable.
out_data_association	An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration.
in_S	The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a MO x MO matrix with M=length of "in_lm_indices_in_S".
in_lm_indices_in_S	The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.

This method will be called just once for each complete KF iteration.

Note: It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.

Parameters

out_z	N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable.
out_data_association	An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration.
in_S	The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M·O x M·O matrix with M=length of "in_lm_indices_in_S".
in_lm_indices_in_S	The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.

This method will be called just once for each complete KF iteration.

Note: It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.

Definition at line 570 of file CRangeBearingKFSLAM.cpp.

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◆ OnInverseObservationModel() [1/3]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnInverseObservationModel	(	const KFArray_OBS &	in_z,
		KFArray_FEAT &	out_yn,
		KFMatrix_FxV &	out_dyn_dxv,
		KFMatrix_FxO &	out_dyn_dhn
	)		const

inlinevirtualinherited

If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".

Parameters

in_z	The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservationsAndDataAssociation().
out_yn	The F-length vector with the inverse observation model .
out_dyn_dxv	The $F \times V$ Jacobian of the inv. sensor model wrt the robot pose $\frac{\partial y_n}{\partial x_v}$ .
out_dyn_dhn	The $F \times O$ Jacobian of the inv. sensor model wrt the observation vector $\frac{\partial y_n}{\partial h_n}$ .

O: OBS_SIZE
V: VEH_SIZE
F: FEAT_SIZE

Note: OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map.

Deprecated:: This version of the method is deprecated. The alternative method is preferred to allow a greater flexibility.

Definition at line 508 of file CKalmanFilterCapable.h.

◆ OnInverseObservationModel() [2/3]

void CRangeBearingKFSLAM::OnInverseObservationModel	(	const KFArray_OBS &	in_z,
		KFArray_FEAT &	out_yn,
		KFMatrix_FxV &	out_dyn_dxv,
		KFMatrix_FxO &	out_dyn_dhn
	)		const

protected

If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".

Parameters

in_z	The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservations().
out_yn	The F-length vector with the inverse observation model .
out_dyn_dxv	The $F \times V$ Jacobian of the inv. sensor model wrt the robot pose $\frac{\partial y_n}{\partial x_v}$ .
out_dyn_dhn	The $F \times O$ Jacobian of the inv. sensor model wrt the observation vector $\frac{\partial y_n}{\partial h_n}$ .

O: OBS_SIZE
V: VEH_SIZE
F: FEAT_SIZE

Note: OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map.

Definition at line 851 of file CRangeBearingKFSLAM.cpp.

References ASSERTMSG_, mrpt::poses::CPose3DQuat::composePoint(), getCurrentRobotPoseMean(), m_SF, MRPT_END, MRPT_START, mrpt::math::UNINITIALIZED_MATRIX, mrpt::math::UNINITIALIZED_QUATERNION, mrpt::math::TPoint3D::x, mrpt::math::TPoint3D::y, and mrpt::math::TPoint3D::z.

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◆ OnInverseObservationModel() [3/3]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnInverseObservationModel	(	const KFArray_OBS &	in_z,
		KFArray_FEAT &	out_yn,
		KFMatrix_FxV &	out_dyn_dxv,
		KFMatrix_FxO &	out_dyn_dhn,
		KFMatrix_FxF &	out_dyn_dhn_R_dyn_dhnT,
		bool &	out_use_dyn_dhn_jacobian
	)		const

inlinevirtualinherited

If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".

The uncertainty in the new map feature comes from two parts: one from the vehicle uncertainty (through the out_dyn_dxv Jacobian), and another from the uncertainty in the observation itself. By default, out_use_dyn_dhn_jacobian=true on call, and if it's left at "true", the base KalmanFilter class will compute the uncertainty of the landmark relative position from out_dyn_dhn. Only in some problems (e.g. MonoSLAM), it'll be needed for the application to directly return the covariance matrix out_dyn_dhn_R_dyn_dhnT, which is the equivalent to:

     \f$ \frac{\partial y_n}{\partial h_n} R \frac{\partial

y_n}{\partial h_n}^\top $. but may be computed from additional terms, or whatever needed by the user. \param in_z The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservationsAndDataAssociation(). \param out_yn The F-length vector with the inverse observation model$ y_n=y(x,z_n) $. \param out_dyn_dxv The$ @_fakenlF \times V $ Jacobian of the inv. sensor model wrt the robot pose $ \frac{\partial y_n}{\partial x_v} $. \param out_dyn_dhn The$ @_fakenlF \times O \frac{\partial y_n}{\partial h_n}

Definition at line 561 of file CKalmanFilterCapable.h.

◆ OnNewLandmarkAddedToMap()

void CRangeBearingKFSLAM::OnNewLandmarkAddedToMap	(	const size_t	in_obsIdx,
		const size_t	in_idxNewFeat
	)

protectedvirtual

If applicable to the given problem, do here any special handling of adding a new landmark to the map.

Parameters

in_obsIndex	The index of the observation whose inverse sensor is to be computed. It corresponds to the row in in_z where the observation can be found.
in_idxNewFeat	The index that this new feature will have in the state vector (0:just after the vehicle state, 1: after that,...). Save this number so data association can be done according to these indices.

See also: OnInverseObservationModel

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

Definition at line 954 of file CRangeBearingKFSLAM.cpp.

References ASSERT_, ASSERTMSG_, mrpt::utils::bimap< KEY, VALUE >::insert(), m_IDs, m_SF, MRPT_END, and MRPT_START.

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◆ OnNormalizeStateVector()

void CRangeBearingKFSLAM::OnNormalizeStateVector ( )

protectedvirtual

This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it.

This virtual function musts normalize the state vector and covariance matrix (only if its necessary).

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

Definition at line 719 of file CRangeBearingKFSLAM.cpp.

References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, and mrpt::math::square().

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◆ OnObservationJacobians() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnObservationJacobians	(	const size_t &	idx_landmark_to_predict,
		KFMatrix_OxV &	Hx,
		KFMatrix_OxF &	Hy
	)		const

inlineprotectedvirtualinherited

Implements the observation Jacobians $\frac{\partial h_i}{\partial x}$ and (when applicable) $\frac{\partial h_i}{\partial y_i}$ .

Parameters

idx_landmark_to_predict	The index of the landmark in the map whose prediction is expected as output. For non SLAM-like problems, this will be zero and the expected output is for the whole state vector.
Hx	The output Jacobian $\frac{\partial h_i}{\partial x}$ .
Hy	The output Jacobian $\frac{\partial h_i}{\partial y_i}$ .

Definition at line 454 of file CKalmanFilterCapable.h.

◆ OnObservationJacobians() [2/2]

void CRangeBearingKFSLAM::OnObservationJacobians	(	const size_t &	idx_landmark_to_predict,
		KFMatrix_OxV &	Hx,
		KFMatrix_OxF &	Hy
	)		const

protected

Implements the observation Jacobians $\frac{\partial h_i}{\partial x}$ and (when applicable) $\frac{\partial h_i}{\partial y_i}$ .

Parameters

idx_landmark_to_predict	The index of the landmark in the map whose prediction is expected as output. For non SLAM-like problems, this will be zero and the expected output is for the whole state vector.
Hx	The output Jacobian $\frac{\partial h_i}{\partial x}$ .
Hy	The output Jacobian $\frac{\partial h_i}{\partial y_i}$ .

Definition at line 491 of file CRangeBearingKFSLAM.cpp.

References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_SF, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose3DQuat::sphericalCoordinates(), mrpt::math::UNINITIALIZED_MATRIX, and mrpt::math::UNINITIALIZED_QUATERNION.

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◆ OnObservationJacobiansNumericGetIncrements()

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnObservationJacobiansNumericGetIncrements	(	KFArray_VEH &	out_veh_increments,
		KFArray_FEAT &	out_feat_increments
	)		const

inlineprotectedvirtualinherited

Only called if using a numeric approximation of the observation Jacobians, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.

Definition at line 468 of file CKalmanFilterCapable.h.

◆ OnObservationModel() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnObservationModel	(	const mrpt::vector_size_t &	idx_landmarks_to_predict,
		vector_KFArray_OBS &	out_predictions
	)		const

protectedpure virtualinherited

Implements the observation prediction $ h_i(x) $ .

Parameters

idx_landmark_to_predict	The indices of the landmarks in the map whose predictions are expected as output. For non SLAM-like problems, this input value is undefined and the application should just generate one observation for the given problem.
out_predictions	The predicted observations.

◆ OnObservationModel() [2/2]

void CRangeBearingKFSLAM::OnObservationModel	(	const vector_size_t &	idx_landmarks_to_predict,
		vector_KFArray_OBS &	out_predictions
	)		const

protected

Definition at line 420 of file CRangeBearingKFSLAM.cpp.

References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_SF, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose3DQuat::size(), and mrpt::poses::CPose3DQuat::sphericalCoordinates().

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◆ OnPostIteration()

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnPostIteration ( )

inlinevirtualinherited

This method is called after finishing one KF iteration and before returning from runOneKalmanIteration().

Definition at line 604 of file CKalmanFilterCapable.h.

◆ OnPreComputingPredictions() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnPreComputingPredictions	(	const vector_KFArray_OBS &	in_all_prediction_means,
		mrpt::vector_size_t &	out_LM_indices_to_predict
	)		const

inlineprotectedvirtualinherited

This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made.

For example, features which are known to be "out of sight" shouldn't be added to the output list to speed up the calculations.

Parameters

in_all_prediction_means	The mean of each landmark predictions; the computation or not of the corresponding covariances is what we're trying to determined with this method.
out_LM_indices_to_predict	The list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.

Note: This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.

See also: OnGetObservations, OnDataAssociation

Definition at line 383 of file CKalmanFilterCapable.h.

◆ OnPreComputingPredictions() [2/2]

void CRangeBearingKFSLAM::OnPreComputingPredictions	(	const vector_KFArray_OBS &	prediction_means,
		vector_size_t &	out_LM_indices_to_predict
	)		const

protected

This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made.

For example, features which are known to be "out of sight" shouldn't be added to the output list to speed up the calculations.

Parameters

in_all_prediction_means	The mean of each landmark predictions; the computation or not of the corresponding covariances is what we're trying to determined with this method.
out_LM_indices_to_predict	The list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.

Note: This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.

See also: OnGetObservations, OnDataAssociation

Definition at line 1390 of file CRangeBearingKFSLAM.cpp.

References ASSERTMSG_, DEG2RAD, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_pitch, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_yaw.

◆ OnSubstractObservationVectors() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnSubstractObservationVectors	(	KFArray_OBS &	A,
		const KFArray_OBS &	B
	)		const

inlineprotectedvirtualinherited

Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).

Definition at line 479 of file CKalmanFilterCapable.h.

◆ OnSubstractObservationVectors() [2/2]

void CRangeBearingKFSLAM::OnSubstractObservationVectors	(	KFArray_OBS &	A,
		const KFArray_OBS &	B
	)		const

protected

Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).

Definition at line 1355 of file CRangeBearingKFSLAM.cpp.

References mrpt::math::wrapToPiInPlace().

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◆ OnTransitionJacobian() [1/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnTransitionJacobian ( KFMatrix_VxV & out_F ) const

inlineprotectedvirtualinherited

Implements the transition Jacobian $\frac{\partial f}{\partial x}$ .

Parameters

out_F Must return the Jacobian. The returned matrix must be $V \times V$ with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems).

Definition at line 345 of file CKalmanFilterCapable.h.

◆ OnTransitionJacobian() [2/2]

void CRangeBearingKFSLAM::OnTransitionJacobian ( KFMatrix_VxV & out_F ) const

protected

Implements the transition Jacobian $\frac{\partial f}{\partial x}$ .

This virtual function musts calculate the Jacobian F of the prediction model.

Parameters

out_F Must return the Jacobian. The returned matrix must be $V \times V$ with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems).

Definition at line 343 of file CRangeBearingKFSLAM.cpp.

References getCurrentRobotPoseMean(), getIncrementFromOdometry(), MRPT_END, MRPT_START, and mrpt::math::UNINITIALIZED_MATRIX.

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◆ OnTransitionJacobianNumericGetIncrements()

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnTransitionJacobianNumericGetIncrements ( KFArray_VEH & out_increments ) const

inlineprotectedvirtualinherited

Only called if using a numeric approximation of the transition Jacobian, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.

Definition at line 355 of file CKalmanFilterCapable.h.

◆ OnTransitionModel() [1/2]

void CRangeBearingKFSLAM::OnTransitionModel	(	const KFArray_ACT &	in_u,
		KFArray_VEH &	inout_x,
		bool &	out_skipPrediction
	)		const

protected

Implements the transition model $\hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k )$ .

This virtual function musts implement the prediction model of the Kalman filter.

Parameters

in_u	The vector returned by OnGetAction.
inout_x	At input has $\hat{x}_{k-1\|k-1}$ , at output must have $\hat{x}_{k\|k-1}$ .
out_skip	Set this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false

Definition at line 304 of file CRangeBearingKFSLAM.cpp.

References mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), mrpt::poses::CPose3DQuat::m_coords, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, and mrpt::math::UNINITIALIZED_QUATERNION.

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◆ OnTransitionModel() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnTransitionModel	(	const KFArray_ACT &	in_u,
		KFArray_VEH &	inout_x,
		bool &	out_skipPrediction
	)		const

protectedpure virtualinherited

Implements the transition model $\hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k )$ .

Parameters

in_u	The vector returned by OnGetAction.
inout_x	At input has $\hat{x}_{k-1\|k-1}$ , at output must have $\hat{x}_{k\|k-1}$ .
out_skip	Set this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false

Note: Even if you return "out_skip=true", the value of "inout_x" MUST be updated as usual (this is to allow numeric approximation of Jacobians).

◆ OnTransitionNoise() [1/2]

void CRangeBearingKFSLAM::OnTransitionNoise ( KFMatrix_VxV & out_Q ) const

protected

Implements the transition noise covariance $ Q_k $ .

This virtual function musts calculate de noise matrix of the prediction model.

Parameters

out_Q Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian

Definition at line 368 of file CRangeBearingKFSLAM.cpp.

References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::changeCoordinatesReference(), mrpt::poses::CPosePDFGaussian::copyFrom(), mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::slam::CRangeBearingKFSLAM::TOptions::force_ignore_odometry, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_action, MRPT_END, MRPT_START, options, mrpt::math::square(), mrpt::slam::CRangeBearingKFSLAM::TOptions::std_odo_z_additional, mrpt::slam::CRangeBearingKFSLAM::TOptions::stds_Q_no_odo, and THROW_EXCEPTION.

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◆ OnTransitionNoise() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnTransitionNoise ( KFMatrix_VxV & out_Q ) const

protectedpure virtualinherited

Implements the transition noise covariance $ Q_k $ .

Parameters

out_Q Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian

◆ processActionObservation()

void CRangeBearingKFSLAM::processActionObservation	(	mrpt::obs::CActionCollection::Ptr &	action,
		mrpt::obs::CSensoryFrame::Ptr &	SF
	)

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.

Parameters

action	May contain odometry
SF	The set of observations, must contain at least one CObservationBearingRange

Definition at line 189 of file CRangeBearingKFSLAM.cpp.

References mrpt::slam::CIncrementalMapPartitioner::addMapFrame(), ASSERT_, mrpt::slam::CRangeBearingKFSLAM::TOptions::create_simplemap, mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPose(), mrpt::maps::CSimpleMap::insert(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::K, m_action, m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, m_SFs, mapPartitioner, MRPT_END, MRPT_START, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::partitioningMethod, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::runOneKalmanIteration(), mrpt::utils::bimap< KEY, VALUE >::size(), mrpt::maps::CSimpleMap::size(), mrpt::math::UNINITIALIZED_QUATERNION, and mrpt::slam::CIncrementalMapPartitioner::updatePartitions().

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◆ reconsiderPartitionsNow()

void CRangeBearingKFSLAM::reconsiderPartitionsNow ( )

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.

See also: getLastPartitionLandmarks

Definition at line 1245 of file CRangeBearingKFSLAM.cpp.

References m_lastPartitionSet, mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::markAllNodesForReconsideration(), and mrpt::slam::CIncrementalMapPartitioner::updatePartitions().

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◆ reset()

void CRangeBearingKFSLAM::reset ( void )

Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0).

Definition at line 61 of file CRangeBearingKFSLAM.cpp.

References mrpt::utils::bimap< KEY, VALUE >::clear(), mrpt::maps::CSimpleMap::clear(), mrpt::slam::CIncrementalMapPartitioner::clear(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), m_action, m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::options, and mrpt::slam::CIncrementalMapPartitioner::TOptions::useMapMatching.

Referenced by CRangeBearingKFSLAM().

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◆ runOneKalmanIteration()

void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::runOneKalmanIteration

protectedinherited

The main entry point, executes one complete step: prediction + update.

It is protected since derived classes must provide a problem-specific entry point for users. The exact order in which this method calls the virtual method is explained in http://www.mrpt.org/Kalman_Filters

Definition at line 649 of file CKalmanFilterCapable_impl.h.

◆ saveMapAndPath2DRepresentationAsMATLABFile()

void CRangeBearingKFSLAM::saveMapAndPath2DRepresentationAsMATLABFile	(	const std::string &	fil,
		float	stdCount = `3.0f`,
		const std::string &	styleLandmarks = `std::string("b")`,
		const std::string &	stylePath = `std::string("r")`,
		const std::string &	styleRobot = `std::string("r")`
	)		const

Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the elements in 2D.

Definition at line 1260 of file CRangeBearingKFSLAM.cpp.

References mrpt::math::cov(), mrpt::system::os::fclose(), mrpt::system::os::fopen(), mrpt::system::os::fprintf(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::math::MATLAB_plotCovariance2D(), mean(), and mrpt::maps::CSimpleMap::size().

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Member Data Documentation

◆ all_predictions

vector_KFArray_OBS mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::all_predictions

privateinherited

Definition at line 628 of file CKalmanFilterCapable.h.

◆ aux_K_dh_dx

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::aux_K_dh_dx

privateinherited

Definition at line 640 of file CKalmanFilterCapable.h.

◆ dh_dx_full_obs

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::dh_dx_full_obs

privateinherited

Definition at line 639 of file CKalmanFilterCapable.h.

◆ Hxs

mrpt::aligned_containers<KFMatrix_OxV>::vector_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::Hxs

privateinherited

The vector of all partial Jacobians dh[i]_dx for each prediction.

Definition at line 631 of file CKalmanFilterCapable.h.

◆ Hys

mrpt::aligned_containers<KFMatrix_OxF>::vector_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::Hys

privateinherited

The vector of all partial Jacobians dh[i]_dy[i] for each prediction.

Definition at line 633 of file CKalmanFilterCapable.h.

◆ K

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::K

privateinherited

Definition at line 637 of file CKalmanFilterCapable.h.

◆ KF_options

TKF_options mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::KF_options

inherited

Generic options for the Kalman Filter algorithm itself.

Definition at line 622 of file CKalmanFilterCapable.h.

◆ m_action

mrpt::obs::CActionCollection::Ptr mrpt::slam::CRangeBearingKFSLAM::m_action

protected

Set up by processActionObservation.

Definition at line 485 of file CRangeBearingKFSLAM.h.

Referenced by getIncrementFromOdometry(), OnTransitionNoise(), processActionObservation(), and reset().

◆ m_IDs

mrpt::utils::bimap<mrpt::maps::CLandmark::TLandmarkID, unsigned int> mrpt::slam::CRangeBearingKFSLAM::m_IDs

protected

The mapping between landmark IDs and indexes in the Pkk cov.

matrix:

Definition at line 491 of file CRangeBearingKFSLAM.h.

Referenced by getAs3DObject(), getCurrentState(), getLastPartitionLandmarks(), OnGetObservationsAndDataAssociation(), OnNewLandmarkAddedToMap(), processActionObservation(), and reset().

◆ m_last_data_association

TDataAssocInfo mrpt::slam::CRangeBearingKFSLAM::m_last_data_association

protected

Last data association.

Definition at line 504 of file CRangeBearingKFSLAM.h.

Referenced by getLastDataAssociation(), and OnGetObservationsAndDataAssociation().

◆ m_lastPartitionSet

std::vector<vector_uint> mrpt::slam::CRangeBearingKFSLAM::m_lastPartitionSet

protected

Definition at line 501 of file CRangeBearingKFSLAM.h.

Referenced by getAs3DObject(), getLastPartition(), getLastPartitionLandmarks(), getLastPartitionLandmarksAsIfFixedSubmaps(), processActionObservation(), reconsiderPartitionsNow(), and reset().

◆ m_pkk

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::m_pkk

protectedinherited

The system full covariance matrix.

Definition at line 309 of file CKalmanFilterCapable.h.

◆ m_SF

mrpt::obs::CSensoryFrame::Ptr mrpt::slam::CRangeBearingKFSLAM::m_SF

protected

Set up by processActionObservation.

Definition at line 488 of file CRangeBearingKFSLAM.h.

Referenced by OnGetObservationsAndDataAssociation(), OnInverseObservationModel(), OnNewLandmarkAddedToMap(), OnObservationJacobians(), OnObservationModel(), OnPreComputingPredictions(), processActionObservation(), and reset().

◆ m_SFs

mrpt::maps::CSimpleMap mrpt::slam::CRangeBearingKFSLAM::m_SFs

protected

The sequence of all the observations and the robot path (kept for debugging, statistics,etc)

Definition at line 499 of file CRangeBearingKFSLAM.h.

Referenced by getAs3DObject(), getLastPartitionLandmarks(), getLastPartitionLandmarksAsIfFixedSubmaps(), processActionObservation(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().

◆ m_timLogger

mrpt::utils::CTimeLogger mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::m_timLogger

protectedinherited

Definition at line 313 of file CKalmanFilterCapable.h.

◆ m_user_didnt_implement_jacobian

bool mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::m_user_didnt_implement_jacobian

mutableprivateinherited

Definition at line 652 of file CKalmanFilterCapable.h.

◆ m_xkk

KFVector mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::m_xkk

protectedinherited

The system state vector.

Definition at line 307 of file CKalmanFilterCapable.h.

◆ mapPartitioner

CIncrementalMapPartitioner mrpt::slam::CRangeBearingKFSLAM::mapPartitioner

protected

Used for map partitioning experiments.

Definition at line 494 of file CRangeBearingKFSLAM.h.

Referenced by loadOptions(), mapPartitionOptions(), processActionObservation(), reconsiderPartitionsNow(), and reset().

◆ options

mrpt::slam::CRangeBearingKFSLAM::TOptions mrpt::slam::CRangeBearingKFSLAM::options

Referenced by getAs3DObject(), getIncrementFromOdometry(), getLastPartitionLandmarks(), loadOptions(), OnGetObservationNoise(), OnGetObservationsAndDataAssociation(), OnPreComputingPredictions(), OnTransitionNoise(), and processActionObservation().

◆ Pkk_subset

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::Pkk_subset

privateinherited

Definition at line 635 of file CKalmanFilterCapable.h.

◆ predictLMidxs

vector_size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::predictLMidxs

privateinherited

Definition at line 629 of file CKalmanFilterCapable.h.

◆ S

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::S

privateinherited

Definition at line 634 of file CKalmanFilterCapable.h.

◆ S_1

KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::S_1

privateinherited

Definition at line 638 of file CKalmanFilterCapable.h.

◆ Z

vector_KFArray_OBS mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::Z

privateinherited

Definition at line 636 of file CKalmanFilterCapable.h.

Detailed Description

Classes

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Static Private Member Functions

Private Attributes

Virtual methods for Kalman Filter implementation

Member Typedef Documentation

◆ KFArray_ACT

◆ KFArray_FEAT

◆ KFArray_OBS

◆ KFArray_VEH

◆ KFCLASS

◆ KFMatrix

◆ KFMatrix_AxA

◆ KFMatrix_FxF

◆ KFMatrix_FxO

◆ KFMatrix_FxV

◆ KFMatrix_OxF

◆ KFMatrix_OxO

◆ KFMatrix_OxV

◆ KFMatrix_VxF

◆ KFMatrix_VxO

◆ KFMatrix_VxV

◆ kftype

◆ KFVector

◆ landmark_point_t

◆ vector_KFArray_OBS

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM()

◆ ~CRangeBearingKFSLAM()

Member Function Documentation

◆ computeOffDiagonalBlocksApproximationError()

◆ get_action_size()

◆ get_feature_size()

◆ get_observation_size()

◆ get_vehicle_size()

◆ getAs3DObject()

◆ getCurrentRobotPose() [1/2]

◆ getCurrentRobotPose() [2/2]

◆ getCurrentRobotPoseMean()

◆ getCurrentState() [1/2]

◆ getCurrentState() [2/2]

◆ getIncrementFromOdometry()

◆ getLandmarkCov()

◆ getLandmarkMean()

◆ getLastDataAssociation()

◆ getLastPartition()

◆ getLastPartitionLandmarks()

◆ getLastPartitionLandmarksAsIfFixedSubmaps()

◆ getNumberOfLandmarksInTheMap()

◆ getProfiler()

◆ getStateVectorLength()

◆ internal_getPkk()

◆ internal_getXkk()

◆ isMapEmpty()

◆ KF_aux_estimate_obs_Hx_jacobian()

◆ KF_aux_estimate_obs_Hy_jacobian()

◆ KF_aux_estimate_trans_jacobian()

◆ loadOptions()

◆ mapPartitionOptions()

◆ OnGetAction() [1/2]

◆ OnGetAction() [2/2]

◆ OnGetObservationNoise() [1/2]

◆ OnGetObservationNoise() [2/2]

◆ OnGetObservationsAndDataAssociation() [1/2]

◆ OnGetObservationsAndDataAssociation() [2/2]

◆ OnInverseObservationModel() [1/3]

◆ OnInverseObservationModel() [2/3]

◆ OnInverseObservationModel() [3/3]

◆ OnNewLandmarkAddedToMap()

◆ OnNormalizeStateVector()

◆ OnObservationJacobians() [1/2]

◆ OnObservationJacobians() [2/2]

◆ OnObservationJacobiansNumericGetIncrements()

◆ OnObservationModel() [1/2]