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mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE > Class Template Reference


Detailed Description

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
class mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >

Virtual base for Kalman Filter (EKF,IEKF,UKF) implementations.

This base class stores the state vector and covariance matrix of the system. It has virtual methods that must be completed by derived classes to address a given filtering problem. The main entry point of the algorithm is CKalmanFilterCapable::runOneKalmanIteration, which should be called AFTER setting the desired filter options in KF_options, as well as any options in the derived class. Note that the main entry point is protected, so derived classes must offer another method more specific to a given problem which, internally, calls runOneKalmanIteration.

For further details and examples, check out the tutorial: http://www.mrpt.org/Kalman_Filters

The Kalman filter algorithms are generic, but this implementation is biased to ease the implementation of SLAM-like problems. However, it can be also applied to many generic problems not related to robotics or SLAM.

The meaning of the template parameters is:

Revisions:

See also:
mrpt::slam::CRangeBearingKFSLAM, mrpt::slam::CRangeBearingKFSLAM2D

Definition at line 159 of file CKalmanFilterCapable.h.

#include <mrpt/bayes/CKalmanFilterCapable.h>

Inheritance diagram for mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >:
Inheritance graph
[legend]

List of all members.

Public Types

typedef KFTYPE kftype
 The numeric type used in the Kalman Filter (default=double)
typedef CKalmanFilterCapable
< VEH_SIZE, OBS_SIZE,
FEAT_SIZE, ACT_SIZE, KFTYPE > 
KFCLASS
 My class, in a shorter name!
typedef
mrpt::dynamicsize_vector
< KFTYPE > 
KFVector
typedef CMatrixTemplateNumeric
< KFTYPE > 
KFMatrix
typedef CMatrixFixedNumeric
< KFTYPE, VEH_SIZE, VEH_SIZE > 
KFMatrix_VxV
typedef CMatrixFixedNumeric
< KFTYPE, OBS_SIZE, OBS_SIZE > 
KFMatrix_OxO
typedef CMatrixFixedNumeric
< KFTYPE, FEAT_SIZE, FEAT_SIZE > 
KFMatrix_FxF
typedef CMatrixFixedNumeric
< KFTYPE, ACT_SIZE, ACT_SIZE > 
KFMatrix_AxA
typedef CMatrixFixedNumeric
< KFTYPE, VEH_SIZE, OBS_SIZE > 
KFMatrix_VxO
typedef CMatrixFixedNumeric
< KFTYPE, VEH_SIZE, FEAT_SIZE > 
KFMatrix_VxF
typedef CMatrixFixedNumeric
< KFTYPE, FEAT_SIZE, VEH_SIZE > 
KFMatrix_FxV
typedef CMatrixFixedNumeric
< KFTYPE, FEAT_SIZE, OBS_SIZE > 
KFMatrix_FxO
typedef CMatrixFixedNumeric
< KFTYPE, OBS_SIZE, FEAT_SIZE > 
KFMatrix_OxF
typedef CMatrixFixedNumeric
< KFTYPE, OBS_SIZE, VEH_SIZE > 
KFMatrix_OxV
typedef CArrayNumeric< KFTYPE,
VEH_SIZE > 
KFArray_VEH
typedef CArrayNumeric< KFTYPE,
ACT_SIZE > 
KFArray_ACT
typedef CArrayNumeric< KFTYPE,
OBS_SIZE > 
KFArray_OBS
typedef
mrpt::aligned_containers
< KFArray_OBS >::vector_t 
vector_KFArray_OBS
typedef CArrayNumeric< KFTYPE,
FEAT_SIZE > 
KFArray_FEAT

Public Member Functions

size_t getNumberOfLandmarksInTheMap () const
bool isMapEmpty () const
size_t getStateVectorLength () const
void getLandmarkMean (size_t idx, KFArray_FEAT &feat) const
 Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems).
void getLandmarkCov (size_t idx, KFMatrix_FxF &feat_cov) const
 Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems).
 CKalmanFilterCapable ()
virtual ~CKalmanFilterCapable ()
 Destructor.
mrpt::utils::CTimeLoggergetProfiler ()

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.

Protected Member Functions

void runOneKalmanIteration ()
 The main entry point, executes one complete step: prediction + update.
Virtual methods for Kalman Filter implementation
virtual void OnGetAction (KFArray_ACT &out_u) const =0
 Must return the action vector u.
virtual void OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const =0
 Implements the transition model $ \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) $.
virtual void OnTransitionJacobian (KFMatrix_VxV &out_F) const
 Implements the transition Jacobian $ \frac{\partial f}{\partial x} $.
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.
virtual void OnTransitionNoise (KFMatrix_VxV &out_Q) const =0
 Implements the transition noise covariance $ Q_k $.
virtual void OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, mrpt::vector_size_t &out_LM_indices_to_predict) const
 This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made.
virtual void OnGetObservationNoise (KFMatrix_OxO &out_R) const =0
 Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.
virtual void OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, mrpt::vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)=0
 This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map.
virtual void OnObservationModel (const mrpt::vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const =0
 Implements the observation prediction $ h_i(x) $.
virtual void OnObservationJacobians (const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const
 Implements the observation Jacobians $ \frac{\partial h_i}{\partial x} $ and (when applicable) $ \frac{\partial h_i}{\partial y_i} $.
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.
virtual void OnSubstractObservationVectors (KFArray_OBS &A, const KFArray_OBS &B) const
 Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).
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".
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".
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.
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.
virtual void OnPostIteration ()
 This method is called after finishing one KF iteration and before returning from runOneKalmanIteration().

Protected Attributes

mrpt::utils::CTimeLogger m_timLogger
Kalman filter state
KFVector m_xkk
 The system state vector.
KFMatrix m_pkk
 The system full covariance matrix.

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

Private Attributes

vector_KFArray_OBS all_predictions
vector_size_t predictLMidxs
KFMatrix dh_dx
KFMatrix dh_dx_full
vector_size_t idxs
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

Friends

struct detail::CRunOneKalmanIteration_addNewLandmarks

Member Typedef Documentation

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CArrayNumeric<KFTYPE,ACT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFArray_ACT

Definition at line 192 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CArrayNumeric<KFTYPE,FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFArray_FEAT

Definition at line 195 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CArrayNumeric<KFTYPE,OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFArray_OBS

Definition at line 193 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CArrayNumeric<KFTYPE,VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFArray_VEH

Definition at line 191 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CKalmanFilterCapable<VEH_SIZE,OBS_SIZE,FEAT_SIZE,ACT_SIZE,KFTYPE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFCLASS

My class, in a shorter name!

Definition at line 171 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixTemplateNumeric<KFTYPE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix

Definition at line 175 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,ACT_SIZE,ACT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_AxA

Definition at line 180 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,FEAT_SIZE,FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_FxF

Definition at line 179 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,FEAT_SIZE,OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_FxO

Definition at line 186 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,FEAT_SIZE,VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_FxV

Definition at line 185 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,OBS_SIZE,FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_OxF

Definition at line 188 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,OBS_SIZE,OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_OxO

Definition at line 178 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,OBS_SIZE,VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_OxV

Definition at line 189 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,VEH_SIZE,FEAT_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_VxF

Definition at line 183 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,VEH_SIZE,OBS_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_VxO

Definition at line 182 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef CMatrixFixedNumeric<KFTYPE,VEH_SIZE,VEH_SIZE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_VxV

Definition at line 177 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef KFTYPE mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::kftype

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

Definition at line 170 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef mrpt::dynamicsize_vector<KFTYPE> mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFVector

Definition at line 174 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
typedef mrpt::aligned_containers<KFArray_OBS>::vector_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::vector_KFArray_OBS

Definition at line 194 of file CKalmanFilterCapable.h.


Constructor & Destructor Documentation

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::CKalmanFilterCapable ( ) [inline]

Default constructor

Definition at line 441 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::~CKalmanFilterCapable ( ) [inline, virtual]

Destructor.

Definition at line 442 of file CKalmanFilterCapable.h.


Member Function Documentation

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::get_action_size ( ) [inline, static]

Definition at line 165 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::get_feature_size ( ) [inline, static]

Definition at line 164 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::get_observation_size ( ) [inline, static]

Definition at line 163 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::get_vehicle_size ( ) [inline, static]

Definition at line 162 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getLandmarkCov ( size_t  idx,
KFMatrix_FxF feat_cov 
) const [inline]

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

Exceptions:
std::exceptionOn idx>= getNumberOfLandmarksInTheMap()

Definition at line 209 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getLandmarkMean ( size_t  idx,
KFArray_FEAT feat 
) const [inline]

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

Exceptions:
std::exceptionOn idx>= getNumberOfLandmarksInTheMap()

Definition at line 202 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getNumberOfLandmarksInTheMap ( ) const [inline]

Definition at line 166 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
mrpt::utils::CTimeLogger& mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getProfiler ( ) [inline]

Definition at line 444 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getStateVectorLength ( ) const [inline]
template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
bool mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::isMapEmpty ( ) const [inline]

Definition at line 167 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KF_aux_estimate_obs_Hx_jacobian ( const KFArray_VEH x,
const std::pair< KFCLASS *, size_t > &  dat,
KFArray_OBS out_x 
) [inline, static, private]

Definition at line 1439 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KF_aux_estimate_obs_Hy_jacobian ( const KFArray_FEAT x,
const std::pair< KFCLASS *, size_t > &  dat,
KFArray_OBS out_x 
) [inline, static, private]

Definition at line 1452 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
static void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KF_aux_estimate_trans_jacobian ( const KFArray_VEH x,
const std::pair< KFCLASS *, KFArray_ACT > &  dat,
KFArray_VEH out_x 
) [inline, static, private]

Auxiliary functions for Jacobian numeric estimation.

Definition at line 1430 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnGetAction ( KFArray_ACT out_u) const [protected, pure virtual]

Must return the action vector u.

Parameters:
out_uThe action vector which will be passed to OnTransitionModel

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnGetObservationNoise ( KFMatrix_OxO out_R) const [protected, pure virtual]

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

Parameters:
out_RThe 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.

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnGetObservationsAndDataAssociation ( vector_KFArray_OBS out_z,
mrpt::vector_int out_data_association,
const vector_KFArray_OBS in_all_predictions,
const KFMatrix in_S,
const vector_size_t in_lm_indices_in_S,
const KFMatrix_OxO in_R 
) [protected, pure virtual]

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_zN 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_associationAn 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_predictionsA 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_SThe 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_SThe 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.

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnInverseObservationModel ( const KFArray_OBS in_z,
KFArray_FEAT out_yn,
KFMatrix_FxV out_dyn_dxv,
KFMatrix_FxO out_dyn_dhn 
) const [inline, protected, virtual]

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

Parameters:
in_zThe observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservationsAndDataAssociation().
out_ynThe F-length vector with the inverse observation model $ y_n=y(x,z_n) $.
out_dyn_dxvThe $F \times V$ Jacobian of the inv. sensor model wrt the robot pose $ \frac{\partial y_n}{\partial x_v} $.
out_dyn_dhnThe $F \times O$ 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.

Reimplemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 363 of file CKalmanFilterCapable.h.

Referenced by mrpt::bayes::detail::CRunOneKalmanIteration_addNewLandmarks::operator()().

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::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 [inline, protected, virtual]

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

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

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

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

Parameters:
in_zThe observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservationsAndDataAssociation().
out_ynThe F-length vector with the inverse observation model $ y_n=y(x,z_n) $.
out_dyn_dxvThe $F \times V$ Jacobian of the inv. sensor model wrt the robot pose $ \frac{\partial y_n}{\partial x_v} $.
out_dyn_dhnThe $F \times O$ Jacobian of the inv. sensor model wrt the observation vector $ \frac{\partial y_n}{\partial h_n} $.
out_dyn_dhn_R_dyn_dhnTSee the discussion above.
  • O: OBS_SIZE
  • V: VEH_SIZE
  • F: FEAT_SIZE
Note:
OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map.

Definition at line 396 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnNewLandmarkAddedToMap ( const size_t  in_obsIdx,
const size_t  in_idxNewFeat 
) [inline, protected, virtual]

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

Parameters:
in_obsIndexThe 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_idxNewFeatThe 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 in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 416 of file CKalmanFilterCapable.h.

Referenced by mrpt::bayes::detail::CRunOneKalmanIteration_addNewLandmarks::operator()().

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnNormalizeStateVector ( ) [inline, protected, virtual]

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

Reimplemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 425 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnObservationJacobians ( const size_t &  idx_landmark_to_predict,
KFMatrix_OxV Hx,
KFMatrix_OxF Hy 
) const [inline, protected, virtual]

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

Parameters:
idx_landmark_to_predictThe 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.
HxThe output Jacobian $ \frac{\partial h_i}{\partial x} $.
HyThe output Jacobian $ \frac{\partial h_i}{\partial y_i} $.

Reimplemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 324 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnObservationJacobiansNumericGetIncrements ( KFArray_VEH out_veh_increments,
KFArray_FEAT out_feat_increments 
) const [inline, protected, virtual]

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 in mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 335 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnObservationModel ( const mrpt::vector_size_t idx_landmarks_to_predict,
vector_KFArray_OBS out_predictions 
) const [protected, pure virtual]

Implements the observation prediction $ h_i(x) $.

Parameters:
idx_landmark_to_predictThe 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_predictionsThe predicted observations.

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnPostIteration ( ) [inline, protected, virtual]

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

Definition at line 432 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnPreComputingPredictions ( const vector_KFArray_OBS in_all_prediction_means,
mrpt::vector_size_t out_LM_indices_to_predict 
) const [inline, protected, virtual]

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_meansThe 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_predictThe list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.
Note:
This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.
See also:
OnGetObservations, OnDataAssociation

Reimplemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 274 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnSubstractObservationVectors ( KFArray_OBS A,
const KFArray_OBS B 
) const [inline, protected, virtual]

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

Reimplemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 345 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnTransitionJacobian ( KFMatrix_VxV out_F) const [inline, protected, virtual]

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

Parameters:
out_FMust 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 in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 249 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnTransitionJacobianNumericGetIncrements ( KFArray_VEH out_increments) const [inline, protected, virtual]

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 in mrpt::slam::CRangeBearingKFSLAM2D.

Definition at line 256 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnTransitionModel ( const KFArray_ACT in_u,
KFArray_VEH inout_x,
bool &  out_skipPrediction 
) const [protected, pure virtual]

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

Parameters:
in_uThe vector returned by OnGetAction.
inout_xAt input has

\[ \hat{x}_{k-1|k-1} \]

, at output must have $ \hat{x}_{k|k-1} $ .

out_skipSet this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false
Note:
Even if you return "out_skip=true", the value of "inout_x" MUST be updated as usual (this is to allow numeric approximation of Jacobians).

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
virtual void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnTransitionNoise ( KFMatrix_VxV out_Q) const [protected, pure virtual]

Implements the transition noise covariance $ Q_k $.

Parameters:
out_QMust return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian

Implemented in mrpt::slam::CRangeBearingKFSLAM, and mrpt::slam::CRangeBearingKFSLAM2D.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
void mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::runOneKalmanIteration ( ) [inline, protected]

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 472 of file CKalmanFilterCapable.h.


Friends And Related Function Documentation

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
friend struct detail::CRunOneKalmanIteration_addNewLandmarks [friend]

Definition at line 1468 of file CKalmanFilterCapable.h.


Member Data Documentation

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
vector_KFArray_OBS mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::all_predictions [private]

Definition at line 453 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::aux_K_dh_dx [private]

Definition at line 464 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::dh_dx [private]

Definition at line 455 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::dh_dx_full [private]

Definition at line 456 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::dh_dx_full_obs [private]

Definition at line 463 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
vector_size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::idxs [private]

Definition at line 457 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::K [private]

Definition at line 461 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
TKF_options mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KF_options

Generic options for the Kalman Filter algorithm itself.

Definition at line 446 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_pkk [protected]

The system full covariance matrix.

Definition at line 218 of file CKalmanFilterCapable.h.

Referenced by mrpt::bayes::detail::CRunOneKalmanIteration_addNewLandmarks::operator()().

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
mrpt::utils::CTimeLogger mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_timLogger [protected]
template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
bool mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_user_didnt_implement_jacobian [mutable, private]

Definition at line 1427 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFVector mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_xkk [protected]

The system state vector.

Definition at line 217 of file CKalmanFilterCapable.h.

Referenced by mrpt::bayes::detail::CRunOneKalmanIteration_addNewLandmarks::operator()().

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::Pkk_subset [private]

Definition at line 459 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
vector_size_t mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::predictLMidxs [private]

Definition at line 454 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::S [private]

Definition at line 458 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
KFMatrix mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::S_1 [private]

Definition at line 462 of file CKalmanFilterCapable.h.

template<size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, typename KFTYPE = double>
vector_KFArray_OBS mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::Z [private]

Definition at line 460 of file CKalmanFilterCapable.h.




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