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 45 of file CRangeBearingKFSLAM2D.h.

#include <mrpt/slam/CRangeBearingKFSLAM2D.h>

Inheritance diagram for mrpt::slam::CRangeBearingKFSLAM2D:

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

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

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

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

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

using	KFVector = Eigen::Matrix< double, Eigen::Dynamic, 1 >

using	KFMatrix = mrpt::math::CMatrixTemplateNumeric< double >

using	KFMatrix_VxV = mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, VEH_SIZE >

using	KFMatrix_OxO = mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, OBS_SIZE >

using	KFMatrix_FxF = mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, FEAT_SIZE >

using	KFMatrix_AxA = mrpt::math::CMatrixFixedNumeric< double, ACT_SIZE, ACT_SIZE >

using	KFMatrix_VxO = mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, OBS_SIZE >

using	KFMatrix_VxF = mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, FEAT_SIZE >

using	KFMatrix_FxV = mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, VEH_SIZE >

using	KFMatrix_FxO = mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, OBS_SIZE >

using	KFMatrix_OxF = mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, FEAT_SIZE >

using	KFMatrix_OxV = mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, VEH_SIZE >

using	KFArray_VEH = mrpt::math::CArrayNumeric< double, VEH_SIZE >

using	KFArray_ACT = mrpt::math::CArrayNumeric< double, ACT_SIZE >

using	KFArray_OBS = mrpt::math::CArrayNumeric< double, OBS_SIZE >

using	vector_KFArray_OBS = mrpt::aligned_std_vector< KFArray_OBS >

using	KFArray_FEAT = mrpt::math::CArrayNumeric< double, FEAT_SIZE >

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 (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::CPosePDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint2D > &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::CPosePDFGaussian &out_robotPose) const
	Returns the mean & 3x3 covariance matrix of the robot 2D pose. 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::config::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

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::system::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
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, mrpt::maps::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, std::vector< int > &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const std::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 std::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	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, std::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::containers::bimap< mrpt::maps::CLandmark::TLandmarkID, unsigned int >	m_IDs
	The mapping between landmark IDs and indexes in the Pkk cov. More...

mrpt::maps::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::system::CTimeLogger	m_timLogger

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

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

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

std::vector< size_t >	predictLMidxs

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

mrpt::aligned_std_vector< KFMatrix_OxF >	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, 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	OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, std::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	OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, std::vector< int > &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const std::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 std::vector< size_t > &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const=0
	Implements the observation prediction . More...

Member Typedef Documentation

◆ KFArray_ACT

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

inherited

Definition at line 268 of file CKalmanFilterCapable.h.

◆ KFArray_FEAT

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

inherited

Definition at line 271 of file CKalmanFilterCapable.h.

◆ KFArray_OBS

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

inherited

Definition at line 269 of file CKalmanFilterCapable.h.

◆ KFArray_VEH

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

inherited

Definition at line 267 of file CKalmanFilterCapable.h.

◆ KFCLASS

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

inherited

My class, in a shorter name!

Definition at line 239 of file CKalmanFilterCapable.h.

◆ KFMatrix

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

inherited

Definition at line 243 of file CKalmanFilterCapable.h.

◆ KFMatrix_AxA

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

inherited

Definition at line 252 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxF

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

inherited

Definition at line 250 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxO

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

inherited

Definition at line 261 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxV

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

inherited

Definition at line 259 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxF

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

inherited

Definition at line 263 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxO

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

inherited

Definition at line 248 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxV

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

inherited

Definition at line 265 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxF

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

inherited

Definition at line 257 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxO

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

inherited

Definition at line 255 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxV

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

inherited

Definition at line 246 of file CKalmanFilterCapable.h.

◆ kftype

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

inherited

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

Definition at line 236 of file CKalmanFilterCapable.h.

◆ KFVector

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

inherited

Definition at line 242 of file CKalmanFilterCapable.h.

◆ landmark_point_t

using mrpt::slam::CRangeBearingKFSLAM2D::landmark_point_t = mrpt::math::TPoint2D

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

Definition at line 53 of file CRangeBearingKFSLAM2D.h.

◆ vector_KFArray_OBS

using mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::vector_KFArray_OBS = mrpt::aligned_std_vector<KFArray_OBS>

inherited

Definition at line 270 of file CKalmanFilterCapable.h.

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM2D()

CRangeBearingKFSLAM2D::CRangeBearingKFSLAM2D ( )

Default constructor.

Definition at line 41 of file CRangeBearingKFSLAM2D.cpp.

References reset().

◆ ~CRangeBearingKFSLAM2D()

CRangeBearingKFSLAM2D::~CRangeBearingKFSLAM2D ( )

virtual

Destructor.

Definition at line 68 of file CRangeBearingKFSLAM2D.cpp.

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 229 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 228 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 227 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 226 of file CKalmanFilterCapable.h.

◆ getAs3DObject()

void CRangeBearingKFSLAM2D::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 950 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::opengl::stock_objects::CornerXYZ(), mrpt::poses::CPoint2DPDFGaussian::cov, mrpt::format(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::poses::CPoint2DPDFGaussian::mean, options, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::quantiles_3D_representation, mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ getCurrentRobotPose()

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

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

See also: getCurrentState

Definition at line 72 of file CRangeBearingKFSLAM2D.cpp.

References ASSERT_, mrpt::poses::CPosePDFGaussian::cov, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::poses::CPosePDFGaussian::mean, MRPT_END, and MRPT_START.

Referenced by processActionObservation().

◆ getCurrentState()

void CRangeBearingKFSLAM2D::getCurrentState	(	mrpt::poses::CPosePDFGaussian &	out_robotPose,
		std::vector< mrpt::math::TPoint2D > &	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 & 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

Definition at line 93 of file CRangeBearingKFSLAM2D.cpp.

References ASSERT_, mrpt::poses::CPosePDFGaussian::cov, mrpt::containers::bimap< KEY, VALUE >::getInverseMap(), m_IDs, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::poses::CPosePDFGaussian::mean, MRPT_END, and MRPT_START.

◆ 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 291 of file CKalmanFilterCapable.h.

◆ getLandmarkIDsFromIndexInStateVector()

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

inlineprotected

Definition at line 372 of file CRangeBearingKFSLAM2D.h.

References mrpt::containers::bimap< KEY, VALUE >::getInverseMap(), and m_IDs.

◆ 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 280 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 190 of file CRangeBearingKFSLAM2D.h.

References m_last_data_association.

◆ getNumberOfLandmarksInTheMap()

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

inlineinherited

Definition at line 230 of file CKalmanFilterCapable.h.

◆ getProfiler()

mrpt::system::CTimeLogger& mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::getProfiler

inlineinherited

Definition at line 616 of file CKalmanFilterCapable.h.

◆ getStateVectorLength()

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

inlineinherited

Definition at line 273 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 275 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 274 of file CKalmanFilterCapable.h.

◆ isMapEmpty()

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

inlineinherited

Definition at line 234 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 1217 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 1234 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 1204 of file CKalmanFilterCapable_impl.h.

◆ loadOptions()

void CRangeBearingKFSLAM2D::loadOptions ( const mrpt::config::CConfigFileBase & ini )

Load options from a ini-like file/text.

Definition at line 732 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::KF_options, mrpt::bayes::TKF_options::loadFromConfigFile(), mrpt::slam::CRangeBearingKFSLAM2D::TOptions::loadFromConfigFile(), and options.

◆ OnGetAction()

void CRangeBearingKFSLAM2D::OnGetAction ( KFArray_ACT & 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 >.

Definition at line 172 of file CRangeBearingKFSLAM2D.cpp.

References m_action, mrpt::poses::CPose3DPDFGaussian::mean, mrpt::poses::CPose2D::phi(), mrpt::obs::CActionRobotMovement3D::poseChange, mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ OnGetObservationNoise()

void 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 >.

Definition at line 1121 of file CRangeBearingKFSLAM2D.cpp.

References options, mrpt::square(), mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_yaw.

◆ OnGetObservationsAndDataAssociation() [1/2]

void CRangeBearingKFSLAM2D::OnGetObservationsAndDataAssociation	(	vector_KFArray_OBS &	Z,
		std::vector< int > &	data_association,
		const vector_KFArray_OBS &	all_predictions,
		const KFMatrix &	S,
		const std::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 526 of file CRangeBearingKFSLAM2D.cpp.

◆ OnGetObservationsAndDataAssociation() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnGetObservationsAndDataAssociation	(	vector_KFArray_OBS &	out_z,
		std::vector< int > &	out_data_association,
		const vector_KFArray_OBS &	in_all_predictions,
		const KFMatrix &	in_S,
		const std::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.

◆ OnInverseObservationModel() [1/2]

void 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 >.

Definition at line 825 of file CRangeBearingKFSLAM2D.cpp.

References ASSERTMSG_, m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ 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

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 557 of file CKalmanFilterCapable.h.

◆ OnNewLandmarkAddedToMap()

void 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 >.

Definition at line 914 of file CRangeBearingKFSLAM2D.cpp.

References ASSERT_, ASSERTMSG_, mrpt::containers::bimap< KEY, VALUE >::insert(), m_IDs, m_last_data_association, m_SF, MRPT_END, MRPT_START, and mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::newly_inserted_landmarks.

◆ OnNormalizeStateVector()

void 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.

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

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

Definition at line 723 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, and mrpt::math::wrapToPiInPlace().

◆ OnObservationJacobians()

void 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 >.

Definition at line 391 of file CRangeBearingKFSLAM2D.cpp.

References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::square(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ OnObservationJacobiansNumericGetIncrements()

void 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 >.

Definition at line 1192 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), and mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size().

◆ OnObservationModel() [1/2]

void CRangeBearingKFSLAM2D::OnObservationModel	(	const std::vector< size_t > &	idx_landmarks_to_predict,
		vector_KFArray_OBS &	out_predictions
	)		const

protected

Definition at line 324 of file CRangeBearingKFSLAM2D.cpp.

References ASSERTDEB_, ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::getNumberOfLandmarksInTheMap(), m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::square(), mrpt::math::wrapToPi(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ OnObservationModel() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnObservationModel	(	const std::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.

◆ 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 600 of file CKalmanFilterCapable.h.

◆ OnPreComputingPredictions() [1/2]

void CRangeBearingKFSLAM2D::OnPreComputingPredictions	(	const vector_KFArray_OBS &	prediction_means,
		std::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 1142 of file CRangeBearingKFSLAM2D.cpp.

References ASSERTMSG_, mrpt::DEG2RAD(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, m_SF, options, STATS_EXPERIMENT, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_yaw.

◆ OnPreComputingPredictions() [2/2]

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnPreComputingPredictions	(	const vector_KFArray_OBS &	in_all_prediction_means,
		std::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 378 of file CKalmanFilterCapable.h.

◆ OnSubstractObservationVectors()

void 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 >.

Definition at line 1108 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::math::wrapToPiInPlace().

◆ OnTransitionJacobian()

void CRangeBearingKFSLAM2D::OnTransitionJacobian ( KFMatrix_VxV & out_F ) const

protectedvirtual

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).

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

Definition at line 234 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::poses::CPose2D::asTPose(), m_action, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, THROW_EXCEPTION, mrpt::math::TPoint2D::x, and mrpt::math::TPoint2D::y.

◆ OnTransitionJacobianNumericGetIncrements()

void 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 >.

Definition at line 1182 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size().

◆ OnTransitionModel()

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

protectedvirtual

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

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

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

Definition at line 203 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

◆ OnTransitionNoise()

void CRangeBearingKFSLAM2D::OnTransitionNoise ( KFMatrix_VxV & out_Q ) const

protectedvirtual

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

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

Definition at line 280 of file CRangeBearingKFSLAM2D.cpp.

References ASSERT_, mrpt::poses::CPosePDFGaussian::cov, m_action, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, options, mrpt::poses::CPosePDFGaussian::rotateCov(), mrpt::square(), mrpt::slam::CRangeBearingKFSLAM2D::TOptions::stds_Q_no_odo, and THROW_EXCEPTION.

◆ processActionObservation()

void CRangeBearingKFSLAM2D::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 138 of file CRangeBearingKFSLAM2D.cpp.

References ASSERT_, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::create_simplemap, getCurrentRobotPose(), mrpt::maps::CSimpleMap::insert(), m_action, m_IDs, m_SF, m_SFs, MRPT_END, MRPT_START, options, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::runOneKalmanIteration(), and mrpt::containers::bimap< KEY, VALUE >::size().

◆ reset()

void CRangeBearingKFSLAM2D::reset ( )

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

Definition at line 50 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::containers::bimap< KEY, VALUE >::clear(), mrpt::maps::CSimpleMap::clear(), m_action, m_IDs, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, m_SF, m_SFs, and mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk.

Referenced by CRangeBearingKFSLAM2D().

◆ 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 27 of file CKalmanFilterCapable_impl.h.

◆ saveMapAndPath2DRepresentationAsMATLABFile()

void 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.

Definition at line 1011 of file CRangeBearingKFSLAM2D.cpp.

References mrpt::math::cov(), mrpt::system::os::fclose(), mrpt::system::os::fopen(), mrpt::system::os::fprintf(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::math::MATLAB_plotCovariance2D(), mean(), mrpt::maps::CSimpleMap::size(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().

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 624 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 636 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 635 of file CKalmanFilterCapable.h.

◆ Hxs

mrpt::aligned_std_vector<KFMatrix_OxV> 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 627 of file CKalmanFilterCapable.h.

◆ Hys

mrpt::aligned_std_vector<KFMatrix_OxF> 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 629 of file CKalmanFilterCapable.h.

◆ K

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

privateinherited

Definition at line 633 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 618 of file CKalmanFilterCapable.h.

◆ m_action

mrpt::obs::CActionCollection::Ptr mrpt::slam::CRangeBearingKFSLAM2D::m_action

protected

Set up by processActionObservation.

Definition at line 381 of file CRangeBearingKFSLAM2D.h.

Referenced by OnGetAction(), OnTransitionJacobian(), OnTransitionNoise(), processActionObservation(), and reset().

◆ m_IDs

mrpt::containers::bimap<mrpt::maps::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 388 of file CRangeBearingKFSLAM2D.h.

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

◆ m_last_data_association

TDataAssocInfo mrpt::slam::CRangeBearingKFSLAM2D::m_last_data_association

protected

Last data association.

Definition at line 395 of file CRangeBearingKFSLAM2D.h.

Referenced by getLastDataAssociation(), OnGetObservationsAndDataAssociation(), and OnNewLandmarkAddedToMap().

◆ 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 304 of file CKalmanFilterCapable.h.

◆ m_SF

mrpt::obs::CSensoryFrame::Ptr mrpt::slam::CRangeBearingKFSLAM2D::m_SF

protected

Set up by processActionObservation.

Definition at line 384 of file CRangeBearingKFSLAM2D.h.

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

◆ m_SFs

mrpt::maps::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 392 of file CRangeBearingKFSLAM2D.h.

Referenced by processActionObservation(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().

◆ m_timLogger

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

protectedinherited

Definition at line 308 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 648 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 302 of file CKalmanFilterCapable.h.

◆ options

TOptions mrpt::slam::CRangeBearingKFSLAM2D::options

The options for the algorithm.

Definition at line 151 of file CRangeBearingKFSLAM2D.h.

Referenced by getAs3DObject(), 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 631 of file CKalmanFilterCapable.h.

◆ predictLMidxs

std::vector<size_t> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::predictLMidxs

privateinherited

Definition at line 625 of file CKalmanFilterCapable.h.

◆ S

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

privateinherited

Definition at line 630 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 634 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 632 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

◆ 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()

◆ internal_getPkk()

◆ internal_getXkk()

◆ isMapEmpty()

◆ KF_aux_estimate_obs_Hx_jacobian()

◆ KF_aux_estimate_obs_Hy_jacobian()

◆ KF_aux_estimate_trans_jacobian()

◆ loadOptions()

◆ OnGetAction()

◆ OnGetObservationNoise()

◆ OnGetObservationsAndDataAssociation() [1/2]

◆ OnGetObservationsAndDataAssociation() [2/2]

◆ OnInverseObservationModel() [1/2]

◆ OnInverseObservationModel() [2/2]

◆ OnNewLandmarkAddedToMap()

◆ OnNormalizeStateVector()

◆ OnObservationJacobians()

◆ OnObservationJacobiansNumericGetIncrements()

◆ OnObservationModel() [1/2]

◆ OnObservationModel() [2/2]

◆ OnPostIteration()

◆ OnPreComputingPredictions() [1/2]

◆ OnPreComputingPredictions() [2/2]

◆ OnSubstractObservationVectors()

◆ OnTransitionJacobian()

◆ OnTransitionJacobianNumericGetIncrements()

◆ OnTransitionModel()

◆ OnTransitionNoise()

◆ processActionObservation()

◆ reset()

◆ runOneKalmanIteration()