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mrpt::slam::CRangeBearingKFSLAM Class Reference
[mrpt-slam] » Metric SLAM algorithms

Detailed Description

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

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

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

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

See also
An implementation for 2D only: CRangeBearingKFSLAM2D

Definition at line 81 of file CRangeBearingKFSLAM.h.

#include <mrpt/slam/CRangeBearingKFSLAM.h>

Inheritance diagram for mrpt::slam::CRangeBearingKFSLAM:
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Classes

struct  TDataAssocInfo
 Information for data-association: More...
 
struct  TOptions
 The options for the algorithm. More...
 

Public Types

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

Public Member Functions

 CRangeBearingKFSLAM ()
 Constructor. More...
 
virtual ~CRangeBearingKFSLAM ()
 Destructor: More...
 
void reset ()
 Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0). More...
 
void processActionObservation (CActionCollectionPtr &action, CSensoryFramePtr &SF)
 Process one new action and observations to update the map and robot pose estimate. More...
 
void getCurrentState (CPose3DQuatPDFGaussian &out_robotPose, std::vector< CPoint3D > &out_landmarksPositions, std::map< unsigned int, CLandmark::TLandmarkID > &out_landmarkIDs, CVectorDouble &out_fullState, CMatrixDouble &out_fullCovariance) const
 Returns the complete mean and cov. More...
 
void getCurrentState (CPose3DPDFGaussian &out_robotPose, std::vector< CPoint3D > &out_landmarksPositions, std::map< unsigned int, CLandmark::TLandmarkID > &out_landmarkIDs, CVectorDouble &out_fullState, CMatrixDouble &out_fullCovariance) const
 Returns the complete mean and cov. More...
 
void getCurrentRobotPose (CPose3DQuatPDFGaussian &out_robotPose) const
 Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with rotation as a quaternion). More...
 
mrpt::poses::CPose3DQuat getCurrentRobotPoseMean () const
 Get the current robot pose mean, as a 3D+quaternion pose. More...
 
void getCurrentRobotPose (CPose3DPDFGaussian &out_robotPose) const
 Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with rotation as 3 angles). More...
 
void getAs3DObject (mrpt::opengl::CSetOfObjectsPtr &outObj) const
 Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state. More...
 
void loadOptions (const mrpt::utils::CConfigFileBase &ini)
 Load options from a ini-like file/text. More...
 
const TDataAssocInfo & getLastDataAssociation () const
 Returns a read-only reference to the information on the last data-association. More...
 
void getLastPartition (std::vector< vector_uint > &parts)
 Return the last partition of the sequence of sensoryframes (it is NOT a partition of the map!!) Only if options.doPartitioningExperiment = true. More...
 
void getLastPartitionLandmarks (std::vector< vector_uint > &landmarksMembership) const
 Return the partitioning of the landmarks in clusters accoring to the last partition. More...
 
void getLastPartitionLandmarksAsIfFixedSubmaps (size_t K, std::vector< vector_uint > &landmarksMembership)
 For testing only: returns the partitioning as "getLastPartitionLandmarks" but as if a fixed-size submaps (size K) were have been used. More...
 
double computeOffDiagonalBlocksApproximationError (const std::vector< vector_uint > &landmarksMembership) const
 Computes the ratio of the missing information matrix elements which are ignored under a certain partitioning of the landmarks. More...
 
void reconsiderPartitionsNow ()
 The partitioning of the entire map is recomputed again. More...
 
CIncrementalMapPartitioner::TOptions * mapPartitionOptions ()
 Provides access to the parameters of the map partitioning algorithm. More...
 
void saveMapAndPath2DRepresentationAsMATLABFile (const std::string &fil, float stdCount=3.0f, const std::string &styleLandmarks=std::string("b"), const std::string &stylePath=std::string("r"), const std::string &styleRobot=std::string("r")) const
 Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the elements in 2D. More...
 
size_t getNumberOfLandmarksInTheMap () const
 
bool isMapEmpty () const
 
size_t getStateVectorLength () const
 
void getLandmarkMean (size_t idx, KFArray_FEAT &feat) const
 Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems). More...
 
void getLandmarkCov (size_t idx, KFMatrix_FxF &feat_cov) const
 Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems). More...
 
mrpt::utils::CTimeLogger & getProfiler ()
 

Static Public Member Functions

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

Public Attributes

mrpt::slam::CRangeBearingKFSLAM::TOptions options
 
TKF_options KF_options
 Generic options for the Kalman Filter algorithm itself. More...
 

Protected Member Functions

mrpt::poses::CPose3DQuat getIncrementFromOdometry () const
 Return the last odometry, as a pose increment. More...
 
void runOneKalmanIteration ()
 The main entry point, executes one complete step: prediction + update. More...
 
Virtual methods for Kalman Filter implementation
void OnGetAction (KFArray_ACT &out_u) const
 Must return the action vector u. More...
 
void OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const
 Implements the transition model $ \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) $. More...
 
void OnTransitionJacobian (KFMatrix_VxV &out_F) const
 Implements the transition Jacobian $ \frac{\partial f}{\partial x} $. More...
 
void OnTransitionNoise (KFMatrix_VxV &out_Q) const
 Implements the transition noise covariance $ Q_k $. More...
 
void OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)
 This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map. More...
 
void OnObservationModel (const vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const
 Implements the observation prediction $ h_i(x) $. 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...
 
void OnGetObservationNoise (KFMatrix_OxO &out_R) const
 Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. More...
 
void OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, vector_size_t &out_LM_indices_to_predict) const
 This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. More...
 
void OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const
 If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...
 
void OnNewLandmarkAddedToMap (const size_t in_obsIdx, const size_t in_idxNewFeat)
 If applicable to the given problem, do here any special handling of adding a new landmark to the map. More...
 
void OnNormalizeStateVector ()
 This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it. More...
 
Virtual methods for Kalman Filter implementation
virtual void 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 OnObservationJacobiansNumericGetIncrements (KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const
 Only called if using a numeric approximation of the observation Jacobians, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian. More...
 
virtual void OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn, KFMatrix_FxF &out_dyn_dhn_R_dyn_dhnT, bool &out_use_dyn_dhn_jacobian) const
 If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...
 
virtual void OnPostIteration ()
 This method is called after finishing one KF iteration and before returning from runOneKalmanIteration(). More...
 

Protected Attributes

CActionCollectionPtr m_action
 Set up by processActionObservation. More...
 
CSensoryFramePtr m_SF
 Set up by processActionObservation. More...
 
mrpt::utils::bimap< CLandmark::TLandmarkID, unsigned int > m_IDs
 The mapping between landmark IDs and indexes in the Pkk cov. More...
 
CIncrementalMapPartitioner mapPartitioner
 Used for map partitioning experiments. More...
 
CSimpleMap m_SFs
 The sequence of all the observations and the robot path (kept for debugging, statistics,etc) More...
 
std::vector< vector_uint > m_lastPartitionSet
 
TDataAssocInfo m_last_data_association
 Last data association. More...
 
mrpt::utils::CTimeLogger m_timLogger
 
Kalman filter state
KFVector m_xkk
 The system state vector. More...
 
KFMatrix m_pkk
 The system full covariance matrix. More...
 

Member Typedef Documentation

◆ KFArray_ACT

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

Definition at line 213 of file CKalmanFilterCapable.h.

◆ KFArray_FEAT

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

Definition at line 216 of file CKalmanFilterCapable.h.

◆ KFArray_OBS

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

Definition at line 214 of file CKalmanFilterCapable.h.

◆ KFArray_VEH

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

Definition at line 212 of file CKalmanFilterCapable.h.

◆ KFCLASS

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

My class, in a shorter name!

Definition at line 192 of file CKalmanFilterCapable.h.

◆ KFMatrix

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

Definition at line 196 of file CKalmanFilterCapable.h.

◆ KFMatrix_AxA

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

Definition at line 201 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxF

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

Definition at line 200 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxO

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

Definition at line 207 of file CKalmanFilterCapable.h.

◆ KFMatrix_FxV

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

Definition at line 206 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxF

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

Definition at line 209 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxO

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

Definition at line 199 of file CKalmanFilterCapable.h.

◆ KFMatrix_OxV

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

Definition at line 210 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxF

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

Definition at line 204 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxO

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

Definition at line 203 of file CKalmanFilterCapable.h.

◆ KFMatrix_VxV

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

Definition at line 198 of file CKalmanFilterCapable.h.

◆ kftype

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

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

Definition at line 191 of file CKalmanFilterCapable.h.

◆ KFVector

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

Definition at line 195 of file CKalmanFilterCapable.h.

◆ vector_KFArray_OBS

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

Definition at line 215 of file CKalmanFilterCapable.h.

Constructor & Destructor Documentation

◆ CRangeBearingKFSLAM()

mrpt::slam::CRangeBearingKFSLAM::CRangeBearingKFSLAM ( )

Constructor.

◆ ~CRangeBearingKFSLAM()

virtual mrpt::slam::CRangeBearingKFSLAM::~CRangeBearingKFSLAM ( )
virtual

Destructor:

Member Function Documentation

◆ computeOffDiagonalBlocksApproximationError()

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

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

See also
getLastPartitionLandmarks, getLastPartitionLandmarksAsIfFixedSubmaps

◆ 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 186 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 185 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 184 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 183 of file CKalmanFilterCapable.h.

◆ getAs3DObject()

void mrpt::slam::CRangeBearingKFSLAM::getAs3DObject ( mrpt::opengl::CSetOfObjectsPtr &  outObj) const

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

Parameters
out_objects

◆ getCurrentRobotPose() [1/2]

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

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

See also
getCurrentState, getCurrentRobotPoseMean

◆ getCurrentRobotPose() [2/2]

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

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

See also
getCurrentState

Definition at line 154 of file CRangeBearingKFSLAM.h.

References mrpt::math::UNINITIALIZED_QUATERNION.

◆ getCurrentRobotPoseMean()

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

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

See also
getCurrentRobotPose

◆ getCurrentState() [1/2]

void mrpt::slam::CRangeBearingKFSLAM::getCurrentState ( CPose3DQuatPDFGaussian &  out_robotPose,
std::vector< CPoint3D > &  out_landmarksPositions,
std::map< unsigned int, CLandmark::TLandmarkID > &  out_landmarkIDs,
CVectorDouble &  out_fullState,
CMatrixDouble &  out_fullCovariance 
) const

Returns the complete mean and cov.

Parameters
out_robotPoseThe mean and the 7x7 covariance matrix of the robot 6D pose
out_landmarksPositionsOne entry for each of the M landmark positions (3D).
out_landmarkIDsEach element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
out_fullStateThe complete state vector (7+3M).
out_fullCovarianceThe full (7+3M)x(7+3M) covariance matrix of the filter.
See also
getCurrentRobotPose

◆ getCurrentState() [2/2]

void mrpt::slam::CRangeBearingKFSLAM::getCurrentState ( CPose3DPDFGaussian &  out_robotPose,
std::vector< CPoint3D > &  out_landmarksPositions,
std::map< unsigned int, CLandmark::TLandmarkID > &  out_landmarkIDs,
CVectorDouble &  out_fullState,
CMatrixDouble &  out_fullCovariance 
) const
inline

Returns the complete mean and cov.

Parameters
out_robotPoseThe mean and the 7x7 covariance matrix of the robot 6D pose
out_landmarksPositionsOne entry for each of the M landmark positions (3D).
out_landmarkIDsEach element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
out_fullStateThe complete state vector (7+3M).
out_fullCovarianceThe full (7+3M)x(7+3M) covariance matrix of the filter.
See also
getCurrentRobotPose

Definition at line 128 of file CRangeBearingKFSLAM.h.

References mrpt::math::UNINITIALIZED_QUATERNION.

◆ getIncrementFromOdometry()

mrpt::poses::CPose3DQuat mrpt::slam::CRangeBearingKFSLAM::getIncrementFromOdometry ( ) const
protected

Return the last odometry, as a pose increment.

◆ 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::exceptionOn idx>= getNumberOfLandmarksInTheMap()

Definition at line 230 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::exceptionOn idx>= getNumberOfLandmarksInTheMap()

Definition at line 223 of file CKalmanFilterCapable.h.

References ASSERT_, and mrpt::system::os::memcpy().

◆ getLastDataAssociation()

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

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

Definition at line 256 of file CRangeBearingKFSLAM.h.

◆ getLastPartition()

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

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

See also
getLastPartitionLandmarks

Definition at line 265 of file CRangeBearingKFSLAM.h.

◆ getLastPartitionLandmarks()

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

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

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

Parameters
landmarksMembershipThe i'th element of this vector is the set of clusters to which the i'th landmark in the map belongs to (landmark index != landmark ID !!).
See also
getLastPartition

◆ getLastPartitionLandmarksAsIfFixedSubmaps()

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

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

◆ getNumberOfLandmarksInTheMap()

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

Definition at line 187 of file CKalmanFilterCapable.h.

References mrpt::bayes::detail::getNumberOfLandmarksInMap().

◆ getProfiler()

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

Definition at line 464 of file CKalmanFilterCapable.h.

◆ getStateVectorLength()

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

Definition at line 218 of file CKalmanFilterCapable.h.

◆ isMapEmpty()

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

Definition at line 188 of file CKalmanFilterCapable.h.

References mrpt::bayes::detail::isMapEmpty().

◆ loadOptions()

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

Load options from a ini-like file/text.

◆ mapPartitionOptions()

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

Provides access to the parameters of the map partitioning algorithm.

Definition at line 300 of file CRangeBearingKFSLAM.h.

◆ OnGetAction()

void mrpt::slam::CRangeBearingKFSLAM::OnGetAction ( KFArray_ACT &  out_u) const
protectedvirtual

Must return the action vector u.

Parameters
out_uThe action vector which will be passed to OnTransitionModel

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnGetObservationNoise()

void mrpt::slam::CRangeBearingKFSLAM::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_RThe noise covariance matrix. It might be non diagonal, but it'll usually be.

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnGetObservationsAndDataAssociation()

void mrpt::slam::CRangeBearingKFSLAM::OnGetObservationsAndDataAssociation ( vector_KFArray_OBS &  out_z,
vector_int &  out_data_association,
const vector_KFArray_OBS &  in_all_predictions,
const KFMatrix &  in_S,
const vector_size_t &  in_lm_indices_in_S,
const KFMatrix_OxO &  in_R 
)
protectedvirtual

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

Parameters
out_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_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.

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ 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,
KFMatrix_FxF &  out_dyn_dhn_R_dyn_dhnT,
bool &  out_use_dyn_dhn_jacobian 
) const
inlineprotectedvirtualinherited

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

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

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

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

Parameters
in_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 417 of file CKalmanFilterCapable.h.

References MRPT_END, and MRPT_START.

◆ OnInverseObservationModel() [2/2]

void mrpt::slam::CRangeBearingKFSLAM::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_zThe observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservations().
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.

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

◆ OnNewLandmarkAddedToMap()

void mrpt::slam::CRangeBearingKFSLAM::OnNewLandmarkAddedToMap ( const size_t  in_obsIdx,
const size_t  in_idxNewFeat 
)
protectedvirtual

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

Parameters
in_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 from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnNormalizeStateVector()

void mrpt::slam::CRangeBearingKFSLAM::OnNormalizeStateVector ( )
protectedvirtual

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

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

◆ OnObservationJacobians()

void mrpt::slam::CRangeBearingKFSLAM::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_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 from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ 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 356 of file CKalmanFilterCapable.h.

◆ OnObservationModel()

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

Implements the observation prediction $ h_i(x) $.

Parameters
idx_landmark_to_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.

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnPostIteration()

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

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

Definition at line 453 of file CKalmanFilterCapable.h.

◆ OnPreComputingPredictions()

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

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

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

Parameters
in_all_prediction_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 from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnSubstractObservationVectors()

void mrpt::slam::CRangeBearingKFSLAM::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< 7, 3, 3, 7 >.

◆ OnTransitionJacobian()

void mrpt::slam::CRangeBearingKFSLAM::OnTransitionJacobian ( KFMatrix_VxV &  out_F) const
protectedvirtual

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

◆ 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 277 of file CKalmanFilterCapable.h.

◆ OnTransitionModel()

void mrpt::slam::CRangeBearingKFSLAM::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_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

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ OnTransitionNoise()

void mrpt::slam::CRangeBearingKFSLAM::OnTransitionNoise ( KFMatrix_VxV &  out_Q) const
protectedvirtual

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

Implements mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.

◆ printf_debug()

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

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

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

◆ processActionObservation()

void mrpt::slam::CRangeBearingKFSLAM::processActionObservation ( CActionCollectionPtr &  action,
CSensoryFramePtr &  SF 
)

Process one new action and observations to update the map and robot pose estimate.

See the description of the class at the top of this page.

Parameters
actionMay contain odometry
SFThe set of observations, must contain at least one CObservationBearingRange

◆ reconsiderPartitionsNow()

void mrpt::slam::CRangeBearingKFSLAM::reconsiderPartitionsNow ( )

The partitioning of the entire map is recomputed again.

Only when options.doPartitioningExperiment = true. This can be used after changing the parameters of the partitioning method. After this method, you can call getLastPartitionLandmarks.

See also
getLastPartitionLandmarks

◆ reset()

void mrpt::slam::CRangeBearingKFSLAM::reset ( )

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

◆ runOneKalmanIteration()

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

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

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

Definition at line 491 of file CKalmanFilterCapable.h.

References Abs(), ASSERT_, ASSERTDEB_, ASSERTMSG_, mrpt::bayes::TKF_options::debug_verify_analytic_jacobians, mrpt::bayes::TKF_options::debug_verify_analytic_jacobians_threshold, mrpt::math::distance(), mrpt::utils::CTimeLogger::enable(), mrpt::bayes::TKF_options::enable_profiler, mrpt::utils::CTimeLogger::enter(), mrpt::math::estimateJacobian(), mrpt::utils::find_in_vector(), mrpt::bayes::TKF_options::IKF_iterations, mrpt::bayes::kfEKFAlaDavison, mrpt::bayes::kfEKFNaive, mrpt::bayes::kfIKF, mrpt::bayes::kfIKFFull, mrpt::utils::CTimeLogger::leave(), mrpt::system::os::memcpy(), mrpt::bayes::TKF_options::method, MRPT_END, MRPT_START, sumAll(), THROW_EXCEPTION, mrpt::math::UNINITIALIZED_MATRIX, mrpt::bayes::TKF_options::use_analytic_observation_jacobian, mrpt::bayes::TKF_options::use_analytic_transition_jacobian, mrpt::mrpt::system::vectorToTextFile(), and mrpt::bayes::TKF_options::verbose.

◆ saveMapAndPath2DRepresentationAsMATLABFile()

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

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

Member Data Documentation

◆ KF_options

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

Generic options for the Kalman Filter algorithm itself.

Definition at line 466 of file CKalmanFilterCapable.h.

◆ m_action

CActionCollectionPtr mrpt::slam::CRangeBearingKFSLAM::m_action
protected

Set up by processActionObservation.

Definition at line 441 of file CRangeBearingKFSLAM.h.

◆ m_IDs

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

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

matrix:

Definition at line 449 of file CRangeBearingKFSLAM.h.

◆ m_last_data_association

TDataAssocInfo mrpt::slam::CRangeBearingKFSLAM::m_last_data_association
protected

Last data association.

Definition at line 462 of file CRangeBearingKFSLAM.h.

◆ m_lastPartitionSet

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

Definition at line 460 of file CRangeBearingKFSLAM.h.

◆ m_pkk

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

The system full covariance matrix.

Definition at line 239 of file CKalmanFilterCapable.h.

◆ m_SF

CSensoryFramePtr mrpt::slam::CRangeBearingKFSLAM::m_SF
protected

Set up by processActionObservation.

Definition at line 445 of file CRangeBearingKFSLAM.h.

◆ m_SFs

CSimpleMap mrpt::slam::CRangeBearingKFSLAM::m_SFs
protected

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

Definition at line 458 of file CRangeBearingKFSLAM.h.

◆ m_timLogger

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

Definition at line 243 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 238 of file CKalmanFilterCapable.h.

◆ mapPartitioner

CIncrementalMapPartitioner mrpt::slam::CRangeBearingKFSLAM::mapPartitioner
protected

Used for map partitioning experiments.

Definition at line 454 of file CRangeBearingKFSLAM.h.

◆ options

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


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