An implementation of EKF-based SLAM with range-bearing sensors, odometry, and a 2D (+heading) robot pose, and 2D landmarks.
The main method is "processActionObservation" which processes pairs of action/observation.
The following pages describe front-end applications based on this class:
- See also
- CRangeBearingKFSLAM
Definition at line 42 of file CRangeBearingKFSLAM2D.h.
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using | landmark_point_t = mrpt::math::TPoint2D |
| Either mrpt::math::TPoint2D or mrpt::math::TPoint3D. More...
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using | kftype = double |
| The numeric type used in the Kalman Filter (default=double) More...
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using | KFCLASS = CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, double > |
| My class, in a shorter name! More...
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using | KFVector = mrpt::math::CVectorDynamic< double > |
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using | KFMatrix = mrpt::math::CMatrixDynamic< double > |
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using | KFMatrix_VxV = mrpt::math::CMatrixFixed< double, VEH_SIZE, VEH_SIZE > |
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using | KFMatrix_OxO = mrpt::math::CMatrixFixed< double, OBS_SIZE, OBS_SIZE > |
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using | KFMatrix_FxF = mrpt::math::CMatrixFixed< double, FEAT_SIZE, FEAT_SIZE > |
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using | KFMatrix_AxA = mrpt::math::CMatrixFixed< double, ACT_SIZE, ACT_SIZE > |
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using | KFMatrix_VxO = mrpt::math::CMatrixFixed< double, VEH_SIZE, OBS_SIZE > |
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using | KFMatrix_VxF = mrpt::math::CMatrixFixed< double, VEH_SIZE, FEAT_SIZE > |
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using | KFMatrix_FxV = mrpt::math::CMatrixFixed< double, FEAT_SIZE, VEH_SIZE > |
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using | KFMatrix_FxO = mrpt::math::CMatrixFixed< double, FEAT_SIZE, OBS_SIZE > |
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using | KFMatrix_OxF = mrpt::math::CMatrixFixed< double, OBS_SIZE, FEAT_SIZE > |
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using | KFMatrix_OxV = mrpt::math::CMatrixFixed< double, OBS_SIZE, VEH_SIZE > |
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using | KFArray_VEH = mrpt::math::CVectorFixed< double, VEH_SIZE > |
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using | KFArray_ACT = mrpt::math::CVectorFixed< double, ACT_SIZE > |
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using | KFArray_OBS = mrpt::math::CVectorFixed< double, OBS_SIZE > |
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using | vector_KFArray_OBS = std::vector< KFArray_OBS > |
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using | KFArray_FEAT = mrpt::math::CVectorFixed< double, FEAT_SIZE > |
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| CRangeBearingKFSLAM2D () |
| Default constructor. More...
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| ~CRangeBearingKFSLAM2D () override |
| Destructor. More...
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void | reset () |
| Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0). More...
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void | processActionObservation (mrpt::obs::CActionCollection::Ptr &action, mrpt::obs::CSensoryFrame::Ptr &SF) |
| Process one new action and observations to update the map and robot pose estimate. More...
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void | getCurrentState (mrpt::poses::CPosePDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint2D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const |
| Returns the complete mean and cov. More...
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void | getCurrentRobotPose (mrpt::poses::CPosePDFGaussian &out_robotPose) const |
| Returns the mean & 3x3 covariance matrix of the robot 2D pose. More...
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void | getAs3DObject (mrpt::opengl::CSetOfObjects::Ptr &outObj) const |
| Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state. More...
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void | loadOptions (const mrpt::config::CConfigFileBase &ini) |
| Load options from a ini-like file/text. More...
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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...
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const TDataAssocInfo & | getLastDataAssociation () const |
| Returns a read-only reference to the information on the last data-association. More...
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size_t | getNumberOfLandmarksInTheMap () const |
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bool | isMapEmpty () const |
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size_t | getStateVectorLength () const |
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KFVector & | internal_getXkk () |
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KFMatrix & | internal_getPkk () |
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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...
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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...
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mrpt::system::CTimeLogger & | getProfiler () |
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void | getLandmarkIDsFromIndexInStateVector (std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_id2index) const |
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void | runOneKalmanIteration () |
| The main entry point, executes one complete step: prediction + update. More...
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void | OnGetAction (KFArray_ACT &out_u) const override |
| Must return the action vector u. More...
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void | OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const override |
| Implements the transition model . More...
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void | OnTransitionJacobian (KFMatrix_VxV &out_F) const override |
| Implements the transition Jacobian . More...
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void | OnTransitionJacobianNumericGetIncrements (KFArray_VEH &out_increments) const override |
| 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...
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void | OnTransitionNoise (KFMatrix_VxV &out_Q) const override |
| Implements the transition noise covariance . More...
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void | OnGetObservationsAndDataAssociation (vector_KFArray_OBS &out_z, std::vector< int > &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const std::vector< size_t > &in_lm_indices_in_S, const KFMatrix_OxO &in_R) override |
| 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...
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void | OnObservationModel (const std::vector< size_t > &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const override |
| Implements the observation prediction . More...
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void | OnObservationJacobians (size_t idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const override |
| Implements the observation Jacobians and (when applicable) . More...
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void | OnObservationJacobiansNumericGetIncrements (KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const override |
| 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...
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void | OnSubstractObservationVectors (KFArray_OBS &A, const KFArray_OBS &B) const override |
| 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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void | OnGetObservationNoise (KFMatrix_OxO &out_R) const override |
| Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. More...
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void | OnPreComputingPredictions (const vector_KFArray_OBS &in_all_prediction_means, std::vector< size_t > &out_LM_indices_to_predict) const override |
| This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. More...
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void | OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const override |
| If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". More...
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void | OnNewLandmarkAddedToMap (const size_t in_obsIdx, const size_t in_idxNewFeat) override |
| If applicable to the given problem, do here any special handling of adding a new landmark to the map. More...
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void | OnNormalizeStateVector () override |
| 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...
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virtual void | OnInverseObservationModel ([[maybe_unused]] const KFArray_OBS &in_z,[[maybe_unused]] KFArray_FEAT &out_yn,[[maybe_unused]] KFMatrix_FxV &out_dyn_dxv,[[maybe_unused]] 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...
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virtual void | OnInverseObservationModel (const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn,[[maybe_unused]] 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...
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virtual void | OnNewLandmarkAddedToMap ([[maybe_unused]] const size_t in_obsIdx,[[maybe_unused]] 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...
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virtual void | OnPostIteration () |
| This method is called after finishing one KF iteration and before returning from runOneKalmanIteration(). More...
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virtual void | OnGetAction (KFArray_ACT &out_u) const=0 |
| Must return the action vector u. More...
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virtual void | OnTransitionModel (const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const=0 |
| Implements the transition model . More...
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virtual void | OnTransitionJacobian ([[maybe_unused]] KFMatrix_VxV &out_F) const |
| Implements the transition Jacobian . More...
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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...
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virtual void | OnPreComputingPredictions ([[maybe_unused]] const vector_KFArray_OBS &in_all_prediction_means, std::vector< size_t > &out_LM_indices_to_predict) const |
| This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. More...
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virtual void | OnObservationJacobians ([[maybe_unused]] size_t idx_landmark_to_predict,[[maybe_unused]] KFMatrix_OxV &Hx,[[maybe_unused]] KFMatrix_OxF &Hy) const |
| Implements the observation Jacobians and (when applicable) . More...
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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...
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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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Returns the complete mean and cov.
- Parameters
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out_robotPose | The mean & 3x3 covariance matrix of the robot 2D pose |
out_landmarksPositions | One entry for each of the M landmark positions (2D). |
out_landmarkIDs | Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID. |
out_fullState | The complete state vector (3+2M). |
out_fullCovariance | The full (3+2M)x(3+2M) covariance matrix of the filter. |
- See also
- getCurrentRobotPose
Definition at line 92 of file CRangeBearingKFSLAM2D.cpp.
References ASSERT_, mrpt::math::CVectorDynamic< T >::begin(), mrpt::math::MatrixBase< Scalar, Derived >::blockCopy(), mrpt::poses::CPosePDFGaussian::cov, mrpt::math::CVectorDynamic< T >::end(), mrpt::containers::bimap< KEY, VALUE >::getInverseMap(), m_IDs, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::poses::CPosePDFGaussian::mean, MRPT_END, MRPT_START, mrpt::math::CVectorDynamic< T >::resize(), and mrpt::math::CVectorDynamic< T >::size().
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
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out_z | N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable. |
out_data_association | An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration. |
in_S | The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M*O x M*O matrix with M=length of "in_lm_indices_in_S". |
in_lm_indices_in_S | The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S. |
This method will be called just once for each complete KF iteration.
- Note
- It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.
- Parameters
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out_z | N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable. |
out_data_association | An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration. |
in_S | The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M·O x M·O matrix with M=length of "in_lm_indices_in_S". |
in_lm_indices_in_S | The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S. |
This method will be called just once for each complete KF iteration.
- Note
- It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.
Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.
Definition at line 519 of file CRangeBearingKFSLAM2D.cpp.
References ASSERTMSG_, mrpt::slam::TDataAssociationResults::associations, mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::clear(), mrpt::slam::CRangeBearingKFSLAM2D::TOptions::data_assoc_IC_chi2_thres, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::data_assoc_IC_metric, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::data_assoc_IC_ml_threshold, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::data_assoc_method, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::data_assoc_metric, mrpt::slam::data_association_full_covariance(), mrpt::containers::bimap< KEY, VALUE >::end(), mrpt::containers::find_in_vector(), mrpt::containers::bimap< KEY, VALUE >::find_key(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_observation_size(), m_IDs, m_last_data_association, m_SF, mrpt::math::mahalanobisDistance2AndLogPDF(), MRPT_END, MRPT_START, options, mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::predictions_IDs, mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::results, mrpt::math::CMatrixDynamic< T >::setSize(), mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::Y_pred_covs, and mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::Y_pred_means.
If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".
- Parameters
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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 . |
- 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 805 of file CRangeBearingKFSLAM2D.cpp.
References ASSERTMSG_, m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::y().
If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".
The uncertainty in the new map feature comes from two parts: one from the vehicle uncertainty (through the out_dyn_dxv Jacobian), and another from the uncertainty in the observation itself. By default, out_use_dyn_dhn_jacobian=true on call, and if it's left at "true", the base KalmanFilter class will compute the uncertainty of the landmark relative position from out_dyn_dhn. Only in some problems (e.g. MonoSLAM), it'll be needed for the application to directly return the covariance matrix out_dyn_dhn_R_dyn_dhnT, which is the equivalent to:
\f$ \frac{\partial y_n}{\partial h_n} R \frac{\partial
y_n}{\partial h_n}^\top
y_n=y(x,z_n)
@_fakenlF \times V
\frac{\partial y_n}{\partial x_v}
@_fakenlF \times O
\frac{\partial y_n}{\partial h_n}
Definition at line 540 of file CKalmanFilterCapable.h.
void CRangeBearingKFSLAM2D::OnNewLandmarkAddedToMap |
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const size_t |
in_obsIdx, |
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const size_t |
in_idxNewFeat |
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) |
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overrideprotected |
If applicable to the given problem, do here any special handling of adding a new landmark to the map.
- Parameters
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in_obsIndex | The index of the observation whose inverse sensor is to be computed. It corresponds to the row in in_z where the observation can be found. |
in_idxNewFeat | The index that this new feature will have in the state vector (0:just after the vehicle state, 1: after that,...). Save this number so data association can be done according to these indices. |
- See also
- OnInverseObservationModel
Definition at line 894 of file CRangeBearingKFSLAM2D.cpp.
References ASSERT_, ASSERTMSG_, mrpt::containers::bimap< KEY, VALUE >::insert(), m_IDs, m_last_data_association, m_SF, MRPT_END, MRPT_START, and mrpt::slam::CRangeBearingKFSLAM2D::TDataAssocInfo::newly_inserted_landmarks.
void CRangeBearingKFSLAM2D::OnNormalizeStateVector |
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overrideprotectedvirtual |
This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it.
This virtual function musts normalize the state vector and covariance matrix (only if its necessary).
Reimplemented from mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.
Definition at line 712 of file CRangeBearingKFSLAM2D.cpp.
References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, and mrpt::math::wrapToPiInPlace().
void CRangeBearingKFSLAM2D::OnObservationJacobians |
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size_t |
idx_landmark_to_predict, |
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KFMatrix_OxV & |
Hx, |
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KFMatrix_OxF & |
Hy |
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) |
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overrideprotected |
Implements the observation Jacobians
and (when applicable)
.
- Parameters
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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 386 of file CRangeBearingKFSLAM2D.cpp.
References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::square(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::y().
void CRangeBearingKFSLAM2D::OnObservationModel |
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const std::vector< size_t > & |
idx_landmarks_to_predict, |
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vector_KFArray_OBS & |
out_predictions |
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) |
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overrideprotectedvirtual |
Implements the observation prediction
.
- Parameters
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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. |
Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.
Definition at line 319 of file CRangeBearingKFSLAM2D.cpp.
References ASSERTDEB_, ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::getNumberOfLandmarksInTheMap(), m_SF, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::square(), mrpt::math::wrapToPi(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::y().
void CRangeBearingKFSLAM2D::OnPreComputingPredictions |
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const vector_KFArray_OBS & |
prediction_means, |
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std::vector< size_t > & |
out_LM_indices_to_predict |
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) |
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overrideprotected |
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
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in_all_prediction_means | The mean of each landmark predictions; the computation or not of the corresponding covariances is what we're trying to determined with this method. |
out_LM_indices_to_predict | The list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted. |
- Note
- This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.
- See also
- OnGetObservations, OnDataAssociation
Definition at line 1115 of file CRangeBearingKFSLAM2D.cpp.
References ASSERTMSG_, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, m_SF, options, STATS_EXPERIMENT, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_range, and mrpt::slam::CRangeBearingKFSLAM2D::TOptions::std_sensor_yaw.
void CRangeBearingKFSLAM2D::OnTransitionJacobian |
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KFMatrix_VxV & |
out_F | ) |
const |
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overrideprotected |
Implements the transition Jacobian
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This virtual function musts calculate the Jacobian F of the prediction model.
- Parameters
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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 229 of file CRangeBearingKFSLAM2D.cpp.
References mrpt::poses::CPose2D::asTPose(), m_action, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, THROW_EXCEPTION, mrpt::math::TPoint2D_data< T >::x, and mrpt::math::TPoint2D_data< T >::y.
void CRangeBearingKFSLAM2D::OnTransitionModel |
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const KFArray_ACT & |
in_u, |
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KFArray_VEH & |
inout_x, |
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bool & |
out_skipPrediction |
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) |
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overrideprotected |
Implements the transition model
.
This virtual function musts implement the prediction model of the Kalman filter.
- Parameters
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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 198 of file CRangeBearingKFSLAM2D.cpp.
References mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, mrpt::poses::CPose2D::phi(), mrpt::math::CVectorDynamic< T >::size(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::y().
void CRangeBearingKFSLAM2D::OnTransitionNoise |
( |
KFMatrix_VxV & |
out_Q | ) |
const |
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overrideprotectedvirtual |
Implements the transition noise covariance
.
This virtual function musts calculate de noise matrix of the prediction model.
- Parameters
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out_Q | Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian |
Implements mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >.
Definition at line 275 of file CRangeBearingKFSLAM2D.cpp.
References ASSERT_, mrpt::poses::CPosePDFGaussian::cov, m_action, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, MRPT_END, MRPT_START, options, mrpt::poses::CPosePDFGaussian::rotateCov(), mrpt::math::CVectorDynamic< T >::size(), mrpt::square(), mrpt::slam::CRangeBearingKFSLAM2D::TOptions::stds_Q_no_odo, and THROW_EXCEPTION.
Process one new action and observations to update the map and robot pose estimate.
See the description of the class at the top of this page.
- Parameters
-
action | May contain odometry |
SF | The set of observations, must contain at least one CObservationBearingRange |
Definition at line 134 of file CRangeBearingKFSLAM2D.cpp.
References ASSERT_, mrpt::slam::CRangeBearingKFSLAM2D::TOptions::create_simplemap, getCurrentRobotPose(), mrpt::maps::CSimpleMap::insert(), m_action, m_IDs, m_SF, m_SFs, MRPT_END, MRPT_START, options, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::runOneKalmanIteration(), and mrpt::containers::bimap< KEY, VALUE >::size().
void CRangeBearingKFSLAM2D::saveMapAndPath2DRepresentationAsMATLABFile |
( |
const std::string & |
fil, |
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float |
stdCount = 3.0f , |
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const std::string & |
styleLandmarks = std::string("b") , |
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const std::string & |
stylePath = std::string("r") , |
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const std::string & |
styleRobot = std::string("r") |
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) |
| 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 984 of file CRangeBearingKFSLAM2D.cpp.
References mrpt::math::cov(), mrpt::system::os::fclose(), mrpt::system::os::fopen(), mrpt::system::os::fprintf(), mrpt::maps::CSimpleMap::get(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_feature_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::get_vehicle_size(), mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_pkk, m_SFs, mrpt::bayes::CKalmanFilterCapable< 3, 2, 2, 3 >::m_xkk, mrpt::math::MATLAB_plotCovariance2D(), mrpt::math::mean(), mrpt::maps::CSimpleMap::size(), mrpt::math::CVectorDynamic< T >::size(), mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::x(), and mrpt::poses::CPoseOrPoint< DERIVEDCLASS, DIM >::y().