Detailed Description

An implementation of EKF-based SLAM with range-bearing sensors, odometry, and a 2D (+heading) robot pose, and 2D landmarks.

The main method is "processActionObservation" which processes pairs of action/observation.

The following pages describe front-end applications based on this class:

See also: CRangeBearingKFSLAM

Definition at line 51 of file CRangeBearingKFSLAM2D.h.

#include <mrpt/slam/CRangeBearingKFSLAM2D.h>

Inheritance diagram for mrpt::slam::CRangeBearingKFSLAM2D:

[legend]

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

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

Public Types
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 mrpt::dynamicsize_vector< double >	KFVector

typedef CMatrixTemplateNumeric< double >	KFMatrix

typedef CMatrixFixedNumeric< double, VEH_SIZE, VEH_SIZE >	KFMatrix_VxV

typedef CMatrixFixedNumeric< double, OBS_SIZE, OBS_SIZE >	KFMatrix_OxO

typedef CMatrixFixedNumeric< double, FEAT_SIZE, FEAT_SIZE >	KFMatrix_FxF

typedef CMatrixFixedNumeric< double, ACT_SIZE, ACT_SIZE >	KFMatrix_AxA

typedef CMatrixFixedNumeric< double, VEH_SIZE, OBS_SIZE >	KFMatrix_VxO

typedef CMatrixFixedNumeric< double, VEH_SIZE, FEAT_SIZE >	KFMatrix_VxF

typedef CMatrixFixedNumeric< double, FEAT_SIZE, VEH_SIZE >	KFMatrix_FxV

typedef CMatrixFixedNumeric< double, FEAT_SIZE, OBS_SIZE >	KFMatrix_FxO

typedef CMatrixFixedNumeric< double, OBS_SIZE, FEAT_SIZE >	KFMatrix_OxF

typedef CMatrixFixedNumeric< double, OBS_SIZE, VEH_SIZE >	KFMatrix_OxV

typedef CArrayNumeric< double, VEH_SIZE >	KFArray_VEH

typedef CArrayNumeric< double, ACT_SIZE >	KFArray_ACT

typedef CArrayNumeric< double, OBS_SIZE >	KFArray_OBS

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

typedef CArrayNumeric< double, FEAT_SIZE >	KFArray_FEAT

Public Member Functions
	CRangeBearingKFSLAM2D ()
	Default constructor. More...

virtual	~CRangeBearingKFSLAM2D ()
	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 (CActionCollectionPtr &action, CSensoryFramePtr &SF)
	Process one new action and observations to update the map and robot pose estimate. More...

void	getCurrentState (CPosePDFGaussian &out_robotPose, std::vector< TPoint2D > &out_landmarksPositions, std::map< unsigned int, CLandmark::TLandmarkID > &out_landmarkIDs, CVectorDouble &out_fullState, CMatrixDouble &out_fullCovariance) const
	Returns the complete mean and cov. More...

void	getCurrentRobotPose (CPosePDFGaussian &out_robotPose) const
	Returns the mean & 3x3 covariance matrix of the robot 2D pose. More...

void	getAs3DObject (mrpt::opengl::CSetOfObjectsPtr &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...

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...

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

size_t	getNumberOfLandmarksInTheMap () const

bool	isMapEmpty () const

size_t	getStateVectorLength () const

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 ()

static void	printf_debug (const char *frmt,...)
	Sends a formated text to "debugOut" if not NULL, or to cout otherwise. More...

Public Attributes
TOptions	options
	The options for the algorithm. More...

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

Protected Member Functions
void	getLandmarkIDsFromIndexInStateVector (std::map< unsigned int, CLandmark::TLandmarkID > &out_id2index) const

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	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...

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
	Implements the observation prediction . More...

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	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...

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...

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, 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...

Protected Attributes
CActionCollectionPtr	m_action
	Set up by processActionObservation. More...

CSensoryFramePtr	m_SF
	Set up by processActionObservation. More...

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

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

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...

Member Typedef Documentation

◆ KFArray_ACT

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

inherited

Definition at line 187 of file CKalmanFilterCapable.h.

◆ KFArray_FEAT

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

inherited

Definition at line 190 of file CKalmanFilterCapable.h.

◆ KFArray_OBS

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

inherited

Definition at line 188 of file CKalmanFilterCapable.h.

◆ KFArray_VEH

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

inherited

Definition at line 186 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 166 of file CKalmanFilterCapable.h.

◆ KFMatrix

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

inherited

Definition at line 170 of file CKalmanFilterCapable.h.

◆ KFMatrix_AxA

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

inherited

Definition at line 175 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxF

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

inherited

Definition at line 174 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxO

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

inherited

Definition at line 181 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxV

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

inherited

Definition at line 180 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxF

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

inherited

Definition at line 183 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxO

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

inherited

Definition at line 173 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxV

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

inherited

Definition at line 184 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxF

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

inherited

Definition at line 178 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxO

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

inherited

Definition at line 177 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxV

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

inherited

Definition at line 172 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 165 of file CKalmanFilterCapable.h.

◆ KFVector

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

inherited

Definition at line 169 of file CKalmanFilterCapable.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 189 of file CKalmanFilterCapable.h.

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM2D()

mrpt::slam::CRangeBearingKFSLAM2D::CRangeBearingKFSLAM2D ( )

Default constructor.

◆ ~CRangeBearingKFSLAM2D()

virtual mrpt::slam::CRangeBearingKFSLAM2D::~CRangeBearingKFSLAM2D ( )

virtual

Destructor.

Member Function Documentation

◆ 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 160 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 159 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 158 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 157 of file CKalmanFilterCapable.h.

◆ getAs3DObject()

void mrpt::slam::CRangeBearingKFSLAM2D::getAs3DObject ( mrpt::opengl::CSetOfObjectsPtr & 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

◆ getCurrentRobotPose()

void mrpt::slam::CRangeBearingKFSLAM2D::getCurrentRobotPose ( CPosePDFGaussian & out_robotPose ) const

Returns the mean & 3x3 covariance matrix of the robot 2D pose.

See also: getCurrentState

◆ getCurrentState()

void mrpt::slam::CRangeBearingKFSLAM2D::getCurrentState	(	CPosePDFGaussian &	out_robotPose,
		std::vector< TPoint2D > &	out_landmarksPositions,
		std::map< unsigned int, CLandmark::TLandmarkID > &	out_landmarkIDs,
		CVectorDouble &	out_fullState,
		CMatrixDouble &	out_fullCovariance
	)		const

Returns the complete mean and cov.

Parameters

out_robotPose	The mean & 3x3 covariance matrix of the robot 2D pose
out_landmarksPositions	One entry for each of the M landmark positions (2D).
out_landmarkIDs	Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
out_fullState	The complete state vector (3+2M).
out_fullCovariance	The full (3+2M)x(3+2M) covariance matrix of the filter.

See also: getCurrentRobotPose

◆ 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 204 of file CKalmanFilterCapable.h.

◆ getLandmarkIDsFromIndexInStateVector()

void mrpt::slam::CRangeBearingKFSLAM2D::getLandmarkIDsFromIndexInStateVector ( std::map< unsigned int, CLandmark::TLandmarkID > & out_id2index ) const

inlineprotected

Definition at line 315 of file CRangeBearingKFSLAM2D.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 197 of file CKalmanFilterCapable.h.

◆ getLastDataAssociation()

const TDataAssocInfo& mrpt::slam::CRangeBearingKFSLAM2D::getLastDataAssociation ( ) const

inline

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

Definition at line 175 of file CRangeBearingKFSLAM2D.h.

◆ getNumberOfLandmarksInTheMap()

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

inlineinherited

Definition at line 161 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 438 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 192 of file CKalmanFilterCapable.h.

◆ isMapEmpty()

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

inlineinherited

Definition at line 162 of file CKalmanFilterCapable.h.

◆ loadOptions()

void mrpt::slam::CRangeBearingKFSLAM2D::loadOptions ( const mrpt::utils::CConfigFileBase & ini )

Load options from a ini-like file/text.

◆ OnGetAction()

void mrpt::slam::CRangeBearingKFSLAM2D::OnGetAction ( KFArray_ACT & out_u ) const

protectedvirtual

Must return the action vector u.

Parameters

out_u The action vector which will be passed to OnTransitionModel

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnGetObservationNoise()

void mrpt::slam::CRangeBearingKFSLAM2D::OnGetObservationNoise ( KFMatrix_OxO & out_R ) const

protectedvirtual

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.

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnGetObservationsAndDataAssociation()

void mrpt::slam::CRangeBearingKFSLAM2D::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
	)

protectedvirtual

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 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.

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnInverseObservationModel() [1/2]

void mrpt::slam::CRangeBearingKFSLAM2D::OnInverseObservationModel	(	const KFArray_OBS &	in_z,
		KFArray_FEAT &	out_yn,
		KFMatrix_FxV &	out_dyn_dxv,
		KFMatrix_FxO &	out_dyn_dhn
	)		const

protectedvirtual

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.

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnInverseObservationModel() [2/2]

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

inlineprotectedvirtualinherited

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:

$\frac{\partial y_n}{\partial h_n} R \frac{\partial y_n}{\partial h_n}^\top$ .

but may be computed from additional terms, or whatever needed by the user.

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 $\frac{\partial y_n}{\partial h_n}$ .
out_dyn_dhn_R_dyn_dhnT	See the discussion above.

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 391 of file CKalmanFilterCapable.h.

◆ OnNewLandmarkAddedToMap()

void mrpt::slam::CRangeBearingKFSLAM2D::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< 3, 2, 2, 3 >.

◆ OnNormalizeStateVector()

void mrpt::slam::CRangeBearingKFSLAM2D::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.

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnObservationJacobians()

void mrpt::slam::CRangeBearingKFSLAM2D::OnObservationJacobians	(	const size_t &	idx_landmark_to_predict,
		KFMatrix_OxV &	Hx,
		KFMatrix_OxF &	Hy
	)		const

protectedvirtual

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 $\frac{\partial h_i}{\partial y_i}$ .

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnObservationJacobiansNumericGetIncrements()

void mrpt::slam::CRangeBearingKFSLAM2D::OnObservationJacobiansNumericGetIncrements	(	KFArray_VEH &	out_veh_increments,
		KFArray_FEAT &	out_feat_increments
	)		const

protectedvirtual

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.

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnObservationModel()

void mrpt::slam::CRangeBearingKFSLAM2D::OnObservationModel	(	const vector_size_t &	idx_landmarks_to_predict,
		vector_KFArray_OBS &	out_predictions
	)		const

protectedvirtual

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.

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnPostIteration()

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

inlineprotectedvirtualinherited

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

Definition at line 427 of file CKalmanFilterCapable.h.

◆ OnPreComputingPredictions()

void mrpt::slam::CRangeBearingKFSLAM2D::OnPreComputingPredictions	(	const vector_KFArray_OBS &	in_all_prediction_means,
		vector_size_t &	out_LM_indices_to_predict
	)		const

protectedvirtual

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

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnSubstractObservationVectors()

void mrpt::slam::CRangeBearingKFSLAM2D::OnSubstractObservationVectors	(	KFArray_OBS &	A,
		const KFArray_OBS &	B
	)		const

protectedvirtual

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

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnTransitionJacobian()

void mrpt::slam::CRangeBearingKFSLAM2D::OnTransitionJacobian ( KFMatrix_VxV & out_F ) const

protectedvirtual

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).

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnTransitionJacobianNumericGetIncrements()

void mrpt::slam::CRangeBearingKFSLAM2D::OnTransitionJacobianNumericGetIncrements ( KFArray_VEH & out_increments ) const

protectedvirtual

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.

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnTransitionModel()

void mrpt::slam::CRangeBearingKFSLAM2D::OnTransitionModel	(	const KFArray_ACT &	in_u,
		KFArray_VEH &	inout_x,
		bool &	out_skipPrediction
	)		const

protectedvirtual

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

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ OnTransitionNoise()

void mrpt::slam::CRangeBearingKFSLAM2D::OnTransitionNoise ( KFMatrix_VxV & out_Q ) const

protectedvirtual

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

Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.

◆ printf_debug()

static void mrpt::utils::CDebugOutputCapable::printf_debug	(	const char *	frmt,
			...
	)

staticinherited

Sends a formated text to "debugOut" if not NULL, or to cout otherwise.

Referenced by mrpt::math::CLevenbergMarquardtTempl< VECTORTYPE, USERPARAM >::execute().

◆ processActionObservation()

void mrpt::slam::CRangeBearingKFSLAM2D::processActionObservation	(	CActionCollectionPtr &	action,
		CSensoryFramePtr &	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

◆ reset()

void mrpt::slam::CRangeBearingKFSLAM2D::reset ( )

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

◆ runOneKalmanIteration()

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

inlineprotectedinherited

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 465 of file CKalmanFilterCapable.h.

◆ saveMapAndPath2DRepresentationAsMATLABFile()

void mrpt::slam::CRangeBearingKFSLAM2D::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.

Member Data Documentation

◆ 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 440 of file CKalmanFilterCapable.h.

◆ m_action

CActionCollectionPtr mrpt::slam::CRangeBearingKFSLAM2D::m_action

protected

Set up by processActionObservation.

Definition at line 324 of file CRangeBearingKFSLAM2D.h.

◆ m_IDs

mrpt::utils::bimap<CLandmark::TLandmarkID,unsigned int> mrpt::slam::CRangeBearingKFSLAM2D::m_IDs

protected

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

matrix:

Definition at line 332 of file CRangeBearingKFSLAM2D.h.

◆ m_last_data_association

TDataAssocInfo mrpt::slam::CRangeBearingKFSLAM2D::m_last_data_association

protected

Last data association.

Definition at line 338 of file CRangeBearingKFSLAM2D.h.

◆ 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 213 of file CKalmanFilterCapable.h.

◆ m_SF

CSensoryFramePtr mrpt::slam::CRangeBearingKFSLAM2D::m_SF

protected

Set up by processActionObservation.

Definition at line 328 of file CRangeBearingKFSLAM2D.h.

◆ m_SFs

CSimpleMap mrpt::slam::CRangeBearingKFSLAM2D::m_SFs

protected

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

Definition at line 336 of file CRangeBearingKFSLAM2D.h.

◆ m_timLogger

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

protectedinherited

Definition at line 217 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 212 of file CKalmanFilterCapable.h.

◆ options

TOptions mrpt::slam::CRangeBearingKFSLAM2D::options

The options for the algorithm.

Definition at line 132 of file CRangeBearingKFSLAM2D.h.

Detailed Description

Classes

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

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

◆ vector_KFArray_OBS

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM2D()

◆ ~CRangeBearingKFSLAM2D()

Member Function Documentation

◆ get_action_size()

◆ get_feature_size()

◆ get_observation_size()

◆ get_vehicle_size()

◆ getAs3DObject()

◆ getCurrentRobotPose()

◆ getCurrentState()

◆ getLandmarkCov()

◆ getLandmarkIDsFromIndexInStateVector()

◆ getLandmarkMean()

◆ getLastDataAssociation()

◆ getNumberOfLandmarksInTheMap()

◆ getProfiler()

◆ getStateVectorLength()

◆ isMapEmpty()

◆ loadOptions()

◆ OnGetAction()

◆ OnGetObservationNoise()

◆ OnGetObservationsAndDataAssociation()

◆ OnInverseObservationModel() [1/2]

◆ OnInverseObservationModel() [2/2]

◆ OnNewLandmarkAddedToMap()

◆ OnNormalizeStateVector()

◆ OnObservationJacobians()

◆ OnObservationJacobiansNumericGetIncrements()

◆ OnObservationModel()

◆ OnPostIteration()

◆ OnPreComputingPredictions()

◆ OnSubstractObservationVectors()

◆ OnTransitionJacobian()

◆ OnTransitionJacobianNumericGetIncrements()

◆ OnTransitionModel()

◆ OnTransitionNoise()

◆ printf_debug()

◆ processActionObservation()

◆ reset()

◆ runOneKalmanIteration()

◆ saveMapAndPath2DRepresentationAsMATLABFile()

Member Data Documentation

◆ KF_options

◆ m_action

◆ m_IDs

◆ m_last_data_association

◆ m_pkk

◆ m_SF

◆ m_SFs

◆ m_timLogger

◆ m_xkk