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
Definition at line 50 of file CRangeBearingKFSLAM.h.
#include <mrpt/slam/CRangeBearingKFSLAM.h>
Classes | |
struct | TDataAssocInfo |
Information for data-association: More... | |
struct | TOptions |
The options for the algorithm. More... | |
Public Types | |
typedef mrpt::math::TPoint3D | landmark_point_t |
Either mrpt::math::TPoint2D or mrpt::math::TPoint3D. More... | |
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 Eigen::Matrix< double, Eigen::Dynamic, 1 > | KFVector |
typedef mrpt::math::CMatrixTemplateNumeric< double > | KFMatrix |
typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, VEH_SIZE > | KFMatrix_VxV |
typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, OBS_SIZE > | KFMatrix_OxO |
typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, FEAT_SIZE > | KFMatrix_FxF |
typedef mrpt::math::CMatrixFixedNumeric< double, ACT_SIZE, ACT_SIZE > | KFMatrix_AxA |
typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, OBS_SIZE > | KFMatrix_VxO |
typedef mrpt::math::CMatrixFixedNumeric< double, VEH_SIZE, FEAT_SIZE > | KFMatrix_VxF |
typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, VEH_SIZE > | KFMatrix_FxV |
typedef mrpt::math::CMatrixFixedNumeric< double, FEAT_SIZE, OBS_SIZE > | KFMatrix_FxO |
typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, FEAT_SIZE > | KFMatrix_OxF |
typedef mrpt::math::CMatrixFixedNumeric< double, OBS_SIZE, VEH_SIZE > | KFMatrix_OxV |
typedef mrpt::math::CArrayNumeric< double, VEH_SIZE > | KFArray_VEH |
typedef mrpt::math::CArrayNumeric< double, ACT_SIZE > | KFArray_ACT |
typedef mrpt::math::CArrayNumeric< double, OBS_SIZE > | KFArray_OBS |
typedef mrpt::aligned_containers< KFArray_OBS >::vector_t | vector_KFArray_OBS |
typedef mrpt::math::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 (mrpt::obs::CActionCollectionPtr &action, mrpt::obs::CSensoryFramePtr &SF) |
Process one new action and observations to update the map and robot pose estimate. More... | |
void | getCurrentState (mrpt::poses::CPose3DQuatPDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint3D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const |
Returns the complete mean and cov. More... | |
void | getCurrentState (mrpt::poses::CPose3DPDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint3D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const |
Returns the complete mean and cov. More... | |
void | getCurrentRobotPose (mrpt::poses::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 (mrpt::poses::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 |
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::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 () |
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 . More... | |
void | OnTransitionJacobian (KFMatrix_VxV &out_F) const |
Implements the transition Jacobian . More... | |
void | OnTransitionNoise (KFMatrix_VxV &out_Q) const |
Implements the transition noise covariance . More... | |
void | OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R) |
This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map. More... | |
void | OnObservationModel (const vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const |
void | OnObservationJacobians (const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const |
Implements the observation Jacobians and (when applicable) . 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... | |
Protected Attributes | |
mrpt::obs::CActionCollectionPtr | m_action |
Set up by processActionObservation. More... | |
mrpt::obs::CSensoryFramePtr | m_SF |
Set up by processActionObservation. More... | |
mrpt::utils::bimap< mrpt::maps::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... | |
mrpt::maps::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... | |
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 | OnPostIteration () |
This method is called after finishing one KF iteration and before returning from runOneKalmanIteration(). More... | |
virtual void | OnGetAction (KFArray_ACT &out_u) const=0 |
Must return the action vector u. More... | |
virtual void | OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const=0 |
Implements the transition model . More... | |
virtual void | OnTransitionJacobian (KFMatrix_VxV &out_F) const |
Implements the transition Jacobian . 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 | OnTransitionNoise (KFMatrix_VxV &out_Q) const=0 |
Implements the transition noise covariance . More... | |
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. More... | |
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. More... | |
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. More... | |
virtual void | OnObservationModel (const mrpt::vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const=0 |
Implements the observation prediction . More... | |
virtual void | OnObservationJacobians (const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const |
Implements the observation Jacobians and (when applicable) . 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 | 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... | |
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Definition at line 193 of file CKalmanFilterCapable.h.
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Definition at line 196 of file CKalmanFilterCapable.h.
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Definition at line 194 of file CKalmanFilterCapable.h.
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Definition at line 192 of file CKalmanFilterCapable.h.
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My class, in a shorter name!
Definition at line 172 of file CKalmanFilterCapable.h.
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Definition at line 176 of file CKalmanFilterCapable.h.
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Definition at line 181 of file CKalmanFilterCapable.h.
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Definition at line 180 of file CKalmanFilterCapable.h.
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Definition at line 187 of file CKalmanFilterCapable.h.
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Definition at line 186 of file CKalmanFilterCapable.h.
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Definition at line 189 of file CKalmanFilterCapable.h.
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Definition at line 179 of file CKalmanFilterCapable.h.
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Definition at line 190 of file CKalmanFilterCapable.h.
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Definition at line 184 of file CKalmanFilterCapable.h.
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Definition at line 183 of file CKalmanFilterCapable.h.
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Definition at line 178 of file CKalmanFilterCapable.h.
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The numeric type used in the Kalman Filter (default=double)
Definition at line 171 of file CKalmanFilterCapable.h.
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Definition at line 175 of file CKalmanFilterCapable.h.
Either mrpt::math::TPoint2D or mrpt::math::TPoint3D.
Definition at line 55 of file CRangeBearingKFSLAM.h.
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Definition at line 195 of file CKalmanFilterCapable.h.
CRangeBearingKFSLAM::CRangeBearingKFSLAM | ( | ) |
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Destructor:
Definition at line 87 of file CRangeBearingKFSLAM.cpp.
double 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.
Definition at line 1119 of file CRangeBearingKFSLAM.cpp.
References ASSERT_, mrpt::math::countCommonElements(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, MRPT_END, MRPT_START, and mrpt::math::sum().
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Definition at line 166 of file CKalmanFilterCapable.h.
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inlinestaticinherited |
Definition at line 165 of file CKalmanFilterCapable.h.
Referenced by computeOffDiagonalBlocksApproximationError(), getAs3DObject(), getCurrentState(), OnObservationJacobians(), OnObservationModel(), processActionObservation(), and saveMapAndPath2DRepresentationAsMATLABFile().
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Definition at line 164 of file CKalmanFilterCapable.h.
Referenced by OnGetObservationsAndDataAssociation().
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Definition at line 163 of file CKalmanFilterCapable.h.
Referenced by computeOffDiagonalBlocksApproximationError(), getAs3DObject(), getCurrentState(), OnObservationJacobians(), OnObservationModel(), OnTransitionModel(), OnTransitionNoise(), processActionObservation(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().
void 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.
out_objects |
Definition at line 924 of file CRangeBearingKFSLAM.cpp.
References mrpt::opengl::stock_objects::CornerXYZ(), mrpt::poses::CPointPDFGaussian::cov, mrpt::opengl::CEllipsoid::Create(), mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::format(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::utils::bimap< KEY, VALUE >::inverse(), m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPointPDFGaussian::mean, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::quantiles_3D_representation, mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS >::y().
void CRangeBearingKFSLAM::getCurrentRobotPose | ( | mrpt::poses::CPose3DQuatPDFGaussian & | out_robotPose | ) | const |
Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with rotation as a quaternion).
Definition at line 95 of file CRangeBearingKFSLAM.cpp.
References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::poses::CPose3DQuat::m_coords, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPose3DQuatPDFGaussian::mean, MRPT_END, and MRPT_START.
Referenced by processActionObservation().
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Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with rotation as 3 angles).
Definition at line 123 of file CRangeBearingKFSLAM.h.
References mrpt::math::UNINITIALIZED_QUATERNION.
mrpt::poses::CPose3DQuat CRangeBearingKFSLAM::getCurrentRobotPoseMean | ( | ) | const |
Get the current robot pose mean, as a 3D+quaternion pose.
Definition at line 120 of file CRangeBearingKFSLAM.cpp.
References ASSERTDEB_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, and mrpt::math::UNINITIALIZED_QUATERNION.
Referenced by OnInverseObservationModel(), OnObservationJacobians(), OnObservationModel(), OnTransitionJacobian(), OnTransitionModel(), and OnTransitionNoise().
void CRangeBearingKFSLAM::getCurrentState | ( | mrpt::poses::CPose3DQuatPDFGaussian & | out_robotPose, |
std::vector< mrpt::math::TPoint3D > & | out_landmarksPositions, | ||
std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > & | out_landmarkIDs, | ||
mrpt::math::CVectorDouble & | out_fullState, | ||
mrpt::math::CMatrixDouble & | out_fullCovariance | ||
) | const |
Returns the complete mean and cov.
out_robotPose | The mean and the 7x7 covariance matrix of the robot 6D pose |
out_landmarksPositions | One entry for each of the M landmark positions (3D). |
out_landmarkIDs | Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID. |
out_fullState | The complete state vector (7+3M). |
out_fullCovariance | The full (7+3M)x(7+3M) covariance matrix of the filter. |
Definition at line 140 of file CRangeBearingKFSLAM.cpp.
References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::utils::bimap< KEY, VALUE >::getInverseMap(), mrpt::poses::CPose3DQuat::m_coords, m_IDs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::poses::CPose3DQuatPDFGaussian::mean, MRPT_END, and MRPT_START.
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Returns the complete mean and cov.
out_robotPose | The mean and the 7x7 covariance matrix of the robot 6D pose |
out_landmarksPositions | One entry for each of the M landmark positions (3D). |
out_landmarkIDs | Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID. |
out_fullState | The complete state vector (7+3M). |
out_fullCovariance | The full (7+3M)x(7+3M) covariance matrix of the filter. |
Definition at line 97 of file CRangeBearingKFSLAM.h.
References mrpt::math::UNINITIALIZED_QUATERNION.
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Return the last odometry, as a pose increment.
Definition at line 266 of file CRangeBearingKFSLAM.cpp.
References mrpt::slam::CRangeBearingKFSLAM::TOptions::force_ignore_odometry, m_action, and options.
Referenced by OnGetAction(), and OnTransitionJacobian().
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Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems).
std::exception | On idx>= getNumberOfLandmarksInTheMap() |
Definition at line 213 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_pkk.
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Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems).
std::exception | On idx>= getNumberOfLandmarksInTheMap() |
Definition at line 206 of file CKalmanFilterCapable.h.
References ASSERT_, mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getNumberOfLandmarksInTheMap(), mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_xkk, and mrpt::system::os::memcpy().
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Returns a read-only reference to the information on the last data-association.
Definition at line 212 of file CRangeBearingKFSLAM.h.
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Return the last partition of the sequence of sensoryframes (it is NOT a partition of the map!!) Only if options.doPartitioningExperiment = true.
Definition at line 221 of file CRangeBearingKFSLAM.h.
void 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
landmarksMembership | The 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 !!). |
Definition at line 1065 of file CRangeBearingKFSLAM.cpp.
References mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::utils::bimap< KEY, VALUE >::inverse(), m_IDs, m_lastPartitionSet, m_SFs, and options.
Referenced by getLastPartitionLandmarksAsIfFixedSubmaps().
void 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.
Definition at line 1032 of file CRangeBearingKFSLAM.cpp.
References getLastPartitionLandmarks(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::K, m_lastPartitionSet, m_SFs, and mrpt::maps::CSimpleMap::size().
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inlineinherited |
Definition at line 167 of file CKalmanFilterCapable.h.
References mrpt::bayes::detail::getNumberOfLandmarksInMap().
Referenced by getAs3DObject(), getLastPartitionLandmarks(), and saveMapAndPath2DRepresentationAsMATLABFile().
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inlineinherited |
Definition at line 460 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_timLogger.
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inlineinherited |
Definition at line 198 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_xkk.
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inlineinherited |
Definition at line 201 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_pkk.
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inlineinherited |
Definition at line 200 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_xkk.
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inlineinherited |
Definition at line 168 of file CKalmanFilterCapable.h.
References mrpt::bayes::detail::isMapEmpty().
void CRangeBearingKFSLAM::loadOptions | ( | const mrpt::utils::CConfigFileBase & | ini | ) |
Load options from a ini-like file/text.
Definition at line 709 of file CRangeBearingKFSLAM.cpp.
References mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::KF_options, mrpt::slam::CIncrementalMapPartitioner::TOptions::loadFromConfigFile(), mrpt::bayes::TKF_options::loadFromConfigFile(), mrpt::slam::CRangeBearingKFSLAM::TOptions::loadFromConfigFile(), mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::options, and options.
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inline |
Provides access to the parameters of the map partitioning algorithm.
Definition at line 256 of file CRangeBearingKFSLAM.h.
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protectedpure virtualinherited |
Must return the action vector u.
out_u | The action vector which will be passed to OnTransitionModel |
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protected |
Must return the action vector u.
out_u | The action vector which will be passed to OnTransitionModel |
Definition at line 289 of file CRangeBearingKFSLAM.cpp.
References getIncrementFromOdometry().
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protectedpure virtualinherited |
Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.
out_R | The noise covariance matrix. It might be non diagonal, but it'll usually be. |
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protected |
Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.
out_R | The noise covariance matrix. It might be non diagonal, but it'll usually be. |
Definition at line 1270 of file CRangeBearingKFSLAM.cpp.
References options, mrpt::math::square(), mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_pitch, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_yaw.
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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.
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.
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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.
out_z | N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable. |
out_data_association | An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration. |
in_S | The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M*O x M*O matrix with M=length of "in_lm_indices_in_S". |
in_lm_indices_in_S | The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S. |
This method will be called just once for each complete KF iteration.
out_z | N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable. |
out_data_association | An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration. |
in_S | The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M·O x M·O matrix with M=length of "in_lm_indices_in_S". |
in_lm_indices_in_S | The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S. |
This method will be called just once for each complete KF iteration.
Definition at line 549 of file CRangeBearingKFSLAM.cpp.
References mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::all_predictions, ASSERTMSG_, mrpt::slam::TDataAssociationResults::associations, mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo::clear(), mrpt::slam::CRangeBearingKFSLAM::TOptions::data_assoc_IC_chi2_thres, mrpt::slam::CRangeBearingKFSLAM::TOptions::data_assoc_IC_metric, mrpt::slam::CRangeBearingKFSLAM::TOptions::data_assoc_IC_ml_threshold, mrpt::slam::CRangeBearingKFSLAM::TOptions::data_assoc_method, mrpt::slam::CRangeBearingKFSLAM::TOptions::data_assoc_metric, mrpt::slam::data_association_full_covariance(), mrpt::utils::bimap< KEY, VALUE >::end(), mrpt::utils::bimap< KEY, VALUE >::find_key(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_observation_size(), m_IDs, m_last_data_association, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, MRPT_END, MRPT_START, options, mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo::predictions_IDs, R, mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo::results, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::S, mrpt::math::UNINITIALIZED_MATRIX, mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo::Y_pred_covs, mrpt::slam::CRangeBearingKFSLAM::TDataAssocInfo::Y_pred_means, and mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::Z.
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inlinevirtualinherited |
If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".
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 Jacobian of the inv. sensor model wrt the robot pose . |
out_dyn_dhn | The Jacobian of the inv. sensor model wrt the observation vector . |
Definition at line 372 of file CKalmanFilterCapable.h.
References MRPT_END, MRPT_START, MRPT_UNUSED_PARAM, and THROW_EXCEPTION.
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protected |
If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".
in_z | The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservations(). |
out_yn | The F-length vector with the inverse observation model . |
out_dyn_dxv | The Jacobian of the inv. sensor model wrt the robot pose . |
out_dyn_dhn | The Jacobian of the inv. sensor model wrt the observation vector . |
Definition at line 799 of file CRangeBearingKFSLAM.cpp.
References ASSERTMSG_, mrpt::poses::CPose3DQuat::composePoint(), getCurrentRobotPoseMean(), m_SF, MRPT_END, MRPT_START, mrpt::math::UNINITIALIZED_MATRIX, mrpt::math::UNINITIALIZED_QUATERNION, mrpt::math::TPoint3D::x, mrpt::math::TPoint3D::y, and mrpt::math::TPoint3D::z.
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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:
.
but may be computed from additional terms, or whatever needed by the user.
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 Jacobian of the inv. sensor model wrt the robot pose . |
out_dyn_dhn | The Jacobian of the inv. sensor model wrt the observation vector . |
out_dyn_dhn_R_dyn_dhnT | See the discussion above. |
Definition at line 407 of file CKalmanFilterCapable.h.
References MRPT_END, MRPT_START, MRPT_UNUSED_PARAM, and mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::OnInverseObservationModel().
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protectedvirtual |
If applicable to the given problem, do here any special handling of adding a new landmark to the map.
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. |
Reimplemented from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.
Definition at line 890 of file CRangeBearingKFSLAM.cpp.
References ASSERT_, ASSERTMSG_, mrpt::utils::bimap< KEY, VALUE >::insert(), m_IDs, m_SF, MRPT_END, and MRPT_START.
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protectedvirtual |
This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it.
This virtual function musts normalize the state vector and covariance matrix (only if its necessary).
Reimplemented from mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >.
Definition at line 689 of file CRangeBearingKFSLAM.cpp.
References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, and mrpt::math::square().
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inlineprotectedvirtualinherited |
Implements the observation Jacobians and (when applicable) .
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 . |
Hy | The output Jacobian . |
Definition at line 330 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_user_didnt_implement_jacobian, and MRPT_UNUSED_PARAM.
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protected |
Implements the observation Jacobians and (when applicable) .
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 . |
Hy | The output Jacobian . |
Definition at line 480 of file CRangeBearingKFSLAM.cpp.
References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_SF, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose3DQuat::sphericalCoordinates(), mrpt::math::UNINITIALIZED_MATRIX, and mrpt::math::UNINITIALIZED_QUATERNION.
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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 342 of file CKalmanFilterCapable.h.
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protectedpure virtualinherited |
Implements the observation prediction .
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. |
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protected |
Definition at line 415 of file CRangeBearingKFSLAM.cpp.
References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_SF, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose3DQuat::size(), and mrpt::poses::CPose3DQuat::sphericalCoordinates().
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inlinevirtualinherited |
This method is called after finishing one KF iteration and before returning from runOneKalmanIteration().
Definition at line 445 of file CKalmanFilterCapable.h.
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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.
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. |
Definition at line 279 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::getNumberOfLandmarksInTheMap(), and MRPT_UNUSED_PARAM.
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protected |
This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made.
For example, features which are known to be "out of sight" shouldn't be added to the output list to speed up the calculations.
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. |
Definition at line 1284 of file CRangeBearingKFSLAM.cpp.
References ASSERTMSG_, DEG2RAD, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_pitch, mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM::TOptions::std_sensor_yaw.
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protected |
Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).
Definition at line 1259 of file CRangeBearingKFSLAM.cpp.
References mrpt::math::wrapToPiInPlace().
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inlineprotectedvirtualinherited |
Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).
Definition at line 352 of file CKalmanFilterCapable.h.
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inlineprotectedvirtualinherited |
Implements the transition Jacobian .
out_F | Must return the Jacobian. The returned matrix must be with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems). |
Definition at line 253 of file CKalmanFilterCapable.h.
References mrpt::bayes::CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::m_user_didnt_implement_jacobian, and MRPT_UNUSED_PARAM.
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protected |
Implements the transition Jacobian .
This virtual function musts calculate the Jacobian F of the prediction model.
out_F | Must return the Jacobian. The returned matrix must be with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems). |
Definition at line 339 of file CRangeBearingKFSLAM.cpp.
References getCurrentRobotPoseMean(), getIncrementFromOdometry(), MRPT_END, MRPT_START, and mrpt::math::UNINITIALIZED_MATRIX.
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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 261 of file CKalmanFilterCapable.h.
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protectedpure virtualinherited |
Implements the transition model .
in_u | The vector returned by OnGetAction. |
inout_x | At input has , at output must have . |
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 |
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protected |
Implements the transition model .
This virtual function musts implement the prediction model of the Kalman filter.
in_u | The vector returned by OnGetAction. |
inout_x | At input has , at output must have . |
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 |
Definition at line 300 of file CRangeBearingKFSLAM.cpp.
References mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), mrpt::poses::CPose3DQuat::m_coords, mrpt::poses::CPose3DQuat::m_quat, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, MRPT_END, MRPT_START, and mrpt::math::UNINITIALIZED_QUATERNION.
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protectedpure virtualinherited |
Implements the transition noise covariance .
out_Q | Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian |
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protected |
Implements the transition noise covariance .
This virtual function musts calculate de noise matrix of the prediction model.
out_Q | Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian |
Definition at line 363 of file CRangeBearingKFSLAM.cpp.
References ASSERT_, mrpt::poses::CPose3DQuatPDFGaussian::changeCoordinatesReference(), mrpt::poses::CPosePDFGaussian::copyFrom(), mrpt::poses::CPose3DQuatPDFGaussian::cov, mrpt::slam::CRangeBearingKFSLAM::TOptions::force_ignore_odometry, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPoseMean(), m_action, MRPT_END, MRPT_START, options, mrpt::math::square(), mrpt::slam::CRangeBearingKFSLAM::TOptions::std_odo_z_additional, mrpt::slam::CRangeBearingKFSLAM::TOptions::stds_Q_no_odo, and THROW_EXCEPTION.
void CRangeBearingKFSLAM::processActionObservation | ( | mrpt::obs::CActionCollectionPtr & | action, |
mrpt::obs::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.
action | May contain odometry |
SF | The set of observations, must contain at least one CObservationBearingRange |
Definition at line 192 of file CRangeBearingKFSLAM.cpp.
References mrpt::slam::CIncrementalMapPartitioner::addMapFrame(), ASSERT_, mrpt::slam::CRangeBearingKFSLAM::TOptions::create_simplemap, mrpt::slam::CRangeBearingKFSLAM::TOptions::doPartitioningExperiment, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), getCurrentRobotPose(), mrpt::maps::CSimpleMap::insert(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::K, m_action, m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, m_SFs, mapPartitioner, MRPT_END, MRPT_START, options, mrpt::slam::CRangeBearingKFSLAM::TOptions::partitioningMethod, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::runOneKalmanIteration(), mrpt::utils::bimap< KEY, VALUE >::size(), mrpt::maps::CSimpleMap::size(), mrpt::math::UNINITIALIZED_QUATERNION, and mrpt::slam::CIncrementalMapPartitioner::updatePartitions().
void 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.
Definition at line 1160 of file CRangeBearingKFSLAM.cpp.
References m_lastPartitionSet, mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::markAllNodesForReconsideration(), and mrpt::slam::CIncrementalMapPartitioner::updatePartitions().
Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0).
Definition at line 61 of file CRangeBearingKFSLAM.cpp.
References mrpt::slam::CIncrementalMapPartitioner::clear(), mrpt::utils::bimap< KEY, VALUE >::clear(), mrpt::maps::CSimpleMap::clear(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), m_action, m_IDs, m_lastPartitionSet, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SF, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mapPartitioner, mrpt::slam::CIncrementalMapPartitioner::options, and mrpt::slam::CIncrementalMapPartitioner::TOptions::useMapMatching.
Referenced by CRangeBearingKFSLAM().
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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 22 of file CKalmanFilterCapable_impl.h.
References mrpt::bayes::detail::addNewLandmarks(), ASSERT_, ASSERTDEB_, ASSERTMSG_, mrpt::math::distance(), mrpt::math::estimateJacobian(), mrpt::utils::find_in_vector(), mrpt::mrpt::format(), mrpt::bayes::kfEKFAlaDavison, mrpt::bayes::kfEKFNaive, mrpt::bayes::kfIKF, mrpt::bayes::kfIKFFull, mrpt::system::os::memcpy(), MRPT_END, MRPT_LOG_DEBUG, MRPT_LOG_WARN_STREAM, MRPT_START, R, mrpt::math::sum(), THROW_EXCEPTION, mrpt::math::UNINITIALIZED_MATRIX, and mrpt::system::vectorToTextFile().
Referenced by processActionObservation().
void 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.
Definition at line 1174 of file CRangeBearingKFSLAM.cpp.
References mrpt::math::cov(), mrpt::system::os::fclose(), mrpt::system::os::fopen(), mrpt::system::os::fprintf(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::getNumberOfLandmarksInTheMap(), mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 7, 3, 3, 7 >::m_xkk, mrpt::math::MATLAB_plotCovariance2D(), mean(), and mrpt::maps::CSimpleMap::size().
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inherited |
Generic options for the Kalman Filter algorithm itself.
Definition at line 462 of file CKalmanFilterCapable.h.
Referenced by loadOptions().
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protected |
Set up by processActionObservation.
Definition at line 396 of file CRangeBearingKFSLAM.h.
Referenced by getIncrementFromOdometry(), OnTransitionNoise(), processActionObservation(), and reset().
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protected |
The mapping between landmark IDs and indexes in the Pkk cov.
matrix:
Definition at line 402 of file CRangeBearingKFSLAM.h.
Referenced by getAs3DObject(), getCurrentState(), getLastPartitionLandmarks(), OnGetObservationsAndDataAssociation(), OnNewLandmarkAddedToMap(), processActionObservation(), and reset().
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protected |
Last data association.
Definition at line 414 of file CRangeBearingKFSLAM.h.
Referenced by OnGetObservationsAndDataAssociation().
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Definition at line 412 of file CRangeBearingKFSLAM.h.
Referenced by getAs3DObject(), getLastPartitionLandmarks(), getLastPartitionLandmarksAsIfFixedSubmaps(), processActionObservation(), reconsiderPartitionsNow(), and reset().
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The system full covariance matrix.
Definition at line 222 of file CKalmanFilterCapable.h.
Referenced by computeOffDiagonalBlocksApproximationError(), getAs3DObject(), getCurrentRobotPose(), getCurrentState(), OnGetObservationsAndDataAssociation(), OnPreComputingPredictions(), processActionObservation(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().
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Set up by processActionObservation.
Definition at line 399 of file CRangeBearingKFSLAM.h.
Referenced by OnGetObservationsAndDataAssociation(), OnInverseObservationModel(), OnNewLandmarkAddedToMap(), OnObservationJacobians(), OnObservationModel(), OnPreComputingPredictions(), processActionObservation(), and reset().
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The sequence of all the observations and the robot path (kept for debugging, statistics,etc)
Definition at line 410 of file CRangeBearingKFSLAM.h.
Referenced by getAs3DObject(), getLastPartitionLandmarks(), getLastPartitionLandmarksAsIfFixedSubmaps(), processActionObservation(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().
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Definition at line 226 of file CKalmanFilterCapable.h.
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The system state vector.
Definition at line 221 of file CKalmanFilterCapable.h.
Referenced by getAs3DObject(), getCurrentRobotPose(), getCurrentRobotPoseMean(), getCurrentState(), OnNormalizeStateVector(), OnObservationJacobians(), OnObservationModel(), OnTransitionModel(), reset(), and saveMapAndPath2DRepresentationAsMATLABFile().
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Used for map partitioning experiments.
Definition at line 406 of file CRangeBearingKFSLAM.h.
Referenced by loadOptions(), processActionObservation(), reconsiderPartitionsNow(), and reset().
mrpt::slam::CRangeBearingKFSLAM::TOptions mrpt::slam::CRangeBearingKFSLAM::options |
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