Detailed Description

Definition at line 54 of file vision_stereo_rectify/test.cpp.

Inheritance diagram for CRangeBearing:

Public Types
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
	CRangeBearing ()

virtual	~CRangeBearing ()

void	doProcess (double DeltaTime, double observationRange, double observationBearing)

void	getState (KFVector &xkk, KFMatrix &pkk)

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

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

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

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

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

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

void	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	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
	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	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...

Protected Attributes
float	m_obsBearing

float	m_obsRange

float	m_deltaTime

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

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

virtual void	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...

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

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

virtual void	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...

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

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.

◆ 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

◆ CRangeBearing()

CRangeBearing::CRangeBearing ( )

Definition at line 425 of file vision_stereo_rectify/test.cpp.

◆ ~CRangeBearing()

CRangeBearing::~CRangeBearing ( )

virtual

Definition at line 444 of file vision_stereo_rectify/test.cpp.

Member Function Documentation

◆ doProcess()

void CRangeBearing::doProcess	(	double	DeltaTime,
		double	observationRange,
		double	observationBearing
	)

Definition at line 445 of file vision_stereo_rectify/test.cpp.

◆ 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.

◆ 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.

◆ 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.

◆ 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.

◆ getState()

void CRangeBearing::getState	(	KFVector &	xkk,
		KFMatrix &	pkk
	)

inline

Definition at line 66 of file vision_stereo_rectify/test.cpp.

◆ 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.

◆ OnGetAction()

void CRangeBearing::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< 4, 2, 0, 1 >.

Definition at line 458 of file vision_stereo_rectify/test.cpp.

◆ OnGetObservationNoise()

void CRangeBearing::OnGetObservationNoise ( KFMatrix_OxO & 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.

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

Implements mrpt::bayes::CKalmanFilterCapable< 4, 2, 0, 1 >.

Definition at line 509 of file vision_stereo_rectify/test.cpp.

◆ OnGetObservationsAndDataAssociation() [1/2]

void CRangeBearing::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
	)

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_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 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 515 of file vision_stereo_rectify/test.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]

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

inlinevirtualinherited

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

Parameters

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

O: OBS_SIZE
V: VEH_SIZE
F: FEAT_SIZE

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

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

Definition at line 504 of file CKalmanFilterCapable.h.

◆ 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()

virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double >::OnNewLandmarkAddedToMap	(	const size_t	in_obsIdx,
		const size_t	in_idxNewFeat
	)

inlinevirtualinherited

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

Definition at line 580 of file CKalmanFilterCapable.h.

◆ OnNormalizeStateVector()

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

inlinevirtualinherited

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.

Definition at line 592 of file CKalmanFilterCapable.h.

◆ OnObservationJacobians()

void CRangeBearing::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 $\frac{\partial h_i}{\partial y_i}$ .
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< 4, 2, 0, 1 >.

Definition at line 559 of file vision_stereo_rectify/test.cpp.

◆ OnObservationJacobiansNumericGetIncrements()

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

inlineprotectedvirtualinherited

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

Definition at line 464 of file CKalmanFilterCapable.h.

◆ OnObservationModel() [1/2]

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

protected

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.

Definition at line 534 of file vision_stereo_rectify/test.cpp.

◆ 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()

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 CRangeBearing::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< 4, 2, 0, 1 >.

Definition at line 580 of file vision_stereo_rectify/test.cpp.

◆ OnTransitionJacobian()

void CRangeBearing::OnTransitionJacobian ( KFMatrix_VxV & F ) const

protectedvirtual

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

Parameters

out_F Must return the Jacobian. The returned matrix must be $N \times N$ with N being either the size of the whole state vector or get_vehicle_size().

Reimplemented from mrpt::bayes::CKalmanFilterCapable< 4, 2, 0, 1 >.

Definition at line 483 of file vision_stereo_rectify/test.cpp.

◆ OnTransitionJacobianNumericGetIncrements()

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

inlineprotectedvirtualinherited

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

Definition at line 350 of file CKalmanFilterCapable.h.

◆ OnTransitionModel()

void CRangeBearing::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< 4, 2, 0, 1 >.

Definition at line 468 of file vision_stereo_rectify/test.cpp.

◆ OnTransitionNoise()

void CRangeBearing::OnTransitionNoise ( KFMatrix_VxV & 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< 4, 2, 0, 1 >.

Definition at line 496 of file vision_stereo_rectify/test.cpp.

◆ 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.

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_deltaTime

float CRangeBearing::m_deltaTime

protected

Definition at line 74 of file vision_stereo_rectify/test.cpp.

◆ m_obsBearing

float CRangeBearing::m_obsBearing

protected

Definition at line 73 of file vision_stereo_rectify/test.cpp.

◆ m_obsRange

float CRangeBearing::m_obsRange

protected

Definition at line 73 of file vision_stereo_rectify/test.cpp.

◆ 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_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.

◆ 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

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

◆ vector_KFArray_OBS

Constructor & Destructor Documentation

◆ CRangeBearing()

◆ ~CRangeBearing()

Member Function Documentation

◆ doProcess()

◆ get_action_size()

◆ get_feature_size()

◆ get_observation_size()

◆ get_vehicle_size()

◆ getLandmarkCov()

◆ getLandmarkMean()

◆ getNumberOfLandmarksInTheMap()

◆ getProfiler()

◆ getState()

◆ getStateVectorLength()

◆ internal_getPkk()

◆ internal_getXkk()

◆ isMapEmpty()

◆ KF_aux_estimate_obs_Hx_jacobian()

◆ KF_aux_estimate_obs_Hy_jacobian()

◆ KF_aux_estimate_trans_jacobian()

◆ 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()

◆ OnSubstractObservationVectors()

◆ OnTransitionJacobian()

◆ OnTransitionJacobianNumericGetIncrements()

◆ OnTransitionModel()

◆ OnTransitionNoise()

◆ runOneKalmanIteration()

Member Data Documentation

◆ all_predictions

◆ aux_K_dh_dx

◆ dh_dx_full_obs

◆ Hxs

◆ Hys

◆ K

◆ KF_options

◆ m_deltaTime