CRangeBearingKFSLAM.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
#ifndef CRangeBearingKFSLAM_H
#define CRangeBearingKFSLAM_H
 
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/config/CConfigFileBase.h>
#include <mrpt/config/CLoadableOptions.h>
#include <mrpt/bayes/CKalmanFilterCapable.h>
 
#include <mrpt/core/safe_pointers.h>
#include <mrpt/containers/bimap.h>
 
#include <mrpt/obs/CSensoryFrame.h>
#include <mrpt/obs/CActionCollection.h>
#include <mrpt/obs/CObservationBearingRange.h>
#include <mrpt/math/lightweight_geom_data.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/poses/CPose3DQuatPDFGaussian.h>
#include <mrpt/maps/CLandmark.h>
#include <mrpt/maps/CSimpleMap.h>
#include <mrpt/slam/CIncrementalMapPartitioner.h>
#include <mrpt/slam/data_association.h>
 
namespace mrpt
{
namespace slam
{
/** 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
  *
  * \sa An implementation for 2D only: CRangeBearingKFSLAM2D
  * \ingroup metric_slam_grp
  */
class CRangeBearingKFSLAM
        : public bayes::CKalmanFilterCapable<7 /* x y z  qr qx qy qz*/,
                                                                                 3 /* range yaw pitch */, 3 /* x y z */,
                                                                                 7 /* Ax Ay Az Aqr Aqx Aqy Aqz */>
// <size_t VEH_SIZE,            size_t OBS_SIZE,        size_t FEAT_SIZE, size_t
// ACT_SIZE, size typename kftype = double>
{
   public:
        /** Either mrpt::math::TPoint2D or mrpt::math::TPoint3D */
        using landmark_point_t = mrpt::math::TPoint3D;
 
        /** Constructor. */
        CRangeBearingKFSLAM();
 
        /** Destructor: */
        virtual ~CRangeBearingKFSLAM();
 
        /** Reset the state of the SLAM filter: The map is emptied and the robot put
         * back to (0,0,0). */
        void reset();
 
        /** 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.
         *  \param action May contain odometry
         *      \param SF The set of observations, must contain at least one
         *CObservationBearingRange
         */
        void processActionObservation(
                mrpt::obs::CActionCollection::Ptr& action,
                mrpt::obs::CSensoryFrame::Ptr& SF);
 
        /** Returns the complete mean and cov.
          *  \param out_robotPose The mean and the 7x7 covariance matrix of the
         * robot 6D pose
          *  \param out_landmarksPositions One entry for each of the M landmark
         * positions (3D).
          *  \param out_landmarkIDs Each element[index] (for indices of
         * out_landmarksPositions) gives the corresponding landmark ID.
          *  \param out_fullState The complete state vector (7+3M).
          *  \param out_fullCovariance The full (7+3M)x(7+3M) covariance matrix of
         * the filter.
          * \sa getCurrentRobotPose
          */
        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.
          *  \param out_robotPose The mean and the 7x7 covariance matrix of the
         * robot 6D pose
          *  \param out_landmarksPositions One entry for each of the M landmark
         * positions (3D).
          *  \param out_landmarkIDs Each element[index] (for indices of
         * out_landmarksPositions) gives the corresponding landmark ID.
          *  \param out_fullState The complete state vector (7+3M).
          *  \param out_fullCovariance The full (7+3M)x(7+3M) covariance matrix of
         * the filter.
          * \sa getCurrentRobotPose
          */
        inline 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
        {
                mrpt::poses::CPose3DQuatPDFGaussian q(
                        mrpt::math::UNINITIALIZED_QUATERNION);
                this->getCurrentState(
                        q, out_landmarksPositions, out_landmarkIDs, out_fullState,
                        out_fullCovariance);
                out_robotPose = mrpt::poses::CPose3DPDFGaussian(q);
        }
 
        /** Returns the mean & the 7x7 covariance matrix of the robot 6D pose (with
         * rotation as a quaternion).
          * \sa getCurrentState, getCurrentRobotPoseMean
          */
        void getCurrentRobotPose(
                mrpt::poses::CPose3DQuatPDFGaussian& out_robotPose) const;
 
        /** Get the current robot pose mean, as a 3D+quaternion pose.
          * \sa getCurrentRobotPose
          */
        mrpt::poses::CPose3DQuat getCurrentRobotPoseMean() const;
 
        /** Returns the mean & the 6x6 covariance matrix of the robot 6D pose (with
         * rotation as 3 angles).
          * \sa getCurrentState
          */
        inline void getCurrentRobotPose(
                mrpt::poses::CPose3DPDFGaussian& out_robotPose) const
        {
                mrpt::poses::CPose3DQuatPDFGaussian q(
                        mrpt::math::UNINITIALIZED_QUATERNION);
                this->getCurrentRobotPose(q);
                out_robotPose = mrpt::poses::CPose3DPDFGaussian(q);
        }
 
        /** Returns a 3D representation of the landmarks in the map and the robot 3D
         * position according to the current filter state.
          *  \param out_objects
          */
        void getAs3DObject(mrpt::opengl::CSetOfObjects::Ptr& outObj) const;
 
        /** Load options from a ini-like file/text
          */
        void loadOptions(const mrpt::config::CConfigFileBase& ini);
 
        /** The options for the algorithm
          */
        struct TOptions : public mrpt::config::CLoadableOptions
        {
                /** Default values */
                TOptions();
 
                void loadFromConfigFile(
                        const mrpt::config::CConfigFileBase& source,
                        const std::string& section) override;  // See base docs
                void dumpToTextStream(
                        std::ostream& out) const override;  // See base docs
 
                /** A 7-length vector with the std. deviation of the transition model in
                 * (x,y,z, qr,qx,qy,qz) used only when there is no odometry (if there is
                 * odo, its uncertainty values will be used instead); x y z: In meters.
                 */
                mrpt::math::CVectorFloat stds_Q_no_odo;
 
                /** The std. deviation of the sensor (for the matrix R in the kalman
                 * filters), in meters and radians. */
                float std_sensor_range, std_sensor_yaw, std_sensor_pitch;
 
                /** Additional std. dev. to sum to the motion model in the z axis
                 * (useful when there is only 2D odometry and we want to put things hard
                 * to the algorithm) (default=0) */
                float std_odo_z_additional;
 
                /** If set to true (default=false), map will be partitioned using the
                 * method stated by partitioningMethod */
                bool doPartitioningExperiment;
 
                /** Default = 3 */
                float quantiles_3D_representation;
 
                /** Applicable only if "doPartitioningExperiment=true".
                  *   0: Automatically detect partition through graph-cut.
                  *   N>=1: Cut every "N" observations.
                  */
                int partitioningMethod;
 
                // Data association:
                TDataAssociationMethod data_assoc_method;
                TDataAssociationMetric data_assoc_metric;
                /** Threshold in [0,1] for the chi2square test for individual
                 * compatibility between predictions and observations (default: 0.99) */
                double data_assoc_IC_chi2_thres;
                /** Whether to use mahalanobis (->chi2 criterion) vs. Matching
                 * likelihood. */
                TDataAssociationMetric data_assoc_IC_metric;
                /** Only if data_assoc_IC_metric==ML, the log-ML threshold (Default=0.0)
                 */
                double data_assoc_IC_ml_threshold;
 
                /** Whether to fill m_SFs (default=false) */
                bool create_simplemap;
 
                /** Whether to ignore the input odometry and behave as if there was no
                 * odometry at all (default: false) */
                bool force_ignore_odometry;
        } options;
 
        /** Information for data-association:
          * \sa getLastDataAssociation
          */
        struct TDataAssocInfo
        {
                TDataAssocInfo() : Y_pred_means(0, 0), Y_pred_covs(0, 0) {}
                void clear()
                {
                        results.clear();
                        predictions_IDs.clear();
                        newly_inserted_landmarks.clear();
                }
 
                // Predictions from the map:
                mrpt::math::CMatrixTemplateNumeric<kftype> Y_pred_means, Y_pred_covs;
                std::vector<size_t> predictions_IDs;
 
                /** Map from the 0-based index within the last observation and the
                   landmark 0-based index in the map (the robot-map state vector)
                        Only used for stats and so. */
                std::map<size_t, size_t> newly_inserted_landmarks;
 
                // DA results:
                TDataAssociationResults results;
        };
 
        /** Returns a read-only reference to the information on the last
         * data-association */
        const TDataAssocInfo& getLastDataAssociation() const
        {
                return m_last_data_association;
        }
 
        /** Return the last partition of the sequence of sensoryframes (it is NOT a
         * partition of the map!!)
          *  Only if options.doPartitioningExperiment = true
          * \sa getLastPartitionLandmarks
          */
        void getLastPartition(std::vector<std::vector<uint32_t>>& parts)
        {
                parts = m_lastPartitionSet;
        }
 
        /** 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
          *  \param 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 !!).
          * \sa getLastPartition
          */
        void getLastPartitionLandmarks(
                std::vector<std::vector<uint32_t>>& landmarksMembership) const;
 
        /** For testing only: returns the partitioning as
         * "getLastPartitionLandmarks" but as if a fixed-size submaps (size K) were
         * have been used.
          */
        void getLastPartitionLandmarksAsIfFixedSubmaps(
                size_t K, std::vector<std::vector<uint32_t>>& landmarksMembership);
 
        /** Computes the ratio of the missing information matrix elements which are
         * ignored under a certain partitioning of the landmarks.
          * \sa getLastPartitionLandmarks, getLastPartitionLandmarksAsIfFixedSubmaps
          */
        double computeOffDiagonalBlocksApproximationError(
                const std::vector<std::vector<uint32_t>>& landmarksMembership) const;
 
        /** 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.
          * \sa getLastPartitionLandmarks
          */
        void reconsiderPartitionsNow();
 
        /** Provides access to the parameters of the map partitioning algorithm.
          */
        CIncrementalMapPartitioner::TOptions* mapPartitionOptions()
        {
                return &mapPartitioner.options;
        }
 
        /** Save the current state of the filter (robot pose & map) to a MATLAB
         * script which displays all the elements in 2D
          */
        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;
 
   protected:
        /** @name Virtual methods for Kalman Filter implementation
                @{
         */
 
        /** Must return the action vector u.
          * \param out_u The action vector which will be passed to OnTransitionModel
          */
        void OnGetAction(KFArray_ACT& out_u) const;
 
        /** Implements the transition model \f$ \hat{x}_{k|k-1} = f(
         * \hat{x}_{k-1|k-1}, u_k ) \f$
          * \param in_u The vector returned by OnGetAction.
          * \param inout_x At input has \f[ \hat{x}_{k-1|k-1} \f] , at output must
         * have \f$ \hat{x}_{k|k-1} \f$ .
          * \param 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
          */
        void OnTransitionModel(
                const KFArray_ACT& in_u, KFArray_VEH& inout_x,
                bool& out_skipPrediction) const;
 
        /** Implements the transition Jacobian \f$ \frac{\partial f}{\partial x} \f$
          * \param out_F Must return the Jacobian.
          *  The returned matrix must be \f$V \times V\f$ with V being either the
         * size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for
         * SLAM problems).
          */
        void OnTransitionJacobian(KFMatrix_VxV& out_F) const;
 
        /** Implements the transition noise covariance \f$ Q_k \f$
          * \param out_Q Must return the covariance matrix.
          *  The returned matrix must be of the same size than the jacobian from
         * OnTransitionJacobian
          */
        void OnTransitionNoise(KFMatrix_VxV& out_Q) const;
 
        /** 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.
          *
          * \param 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.
          * \param 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.
          * \param 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".
          * \param 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.
          */
        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);
 
        void OnObservationModel(
                const std::vector<size_t>& idx_landmarks_to_predict,
                vector_KFArray_OBS& out_predictions) const;
 
        /** Implements the observation Jacobians \f$ \frac{\partial h_i}{\partial x}
         * \f$ and (when applicable) \f$ \frac{\partial h_i}{\partial y_i} \f$.
          * \param idx_landmark_to_predict The index of the landmark in the map
         * whose prediction is expected as output. For non SLAM-like problems, this
         * will be zero and the expected output is for the whole state vector.
          * \param Hx  The output Jacobian \f$ \frac{\partial h_i}{\partial x} \f$.
          * \param Hy  The output Jacobian \f$ \frac{\partial h_i}{\partial y_i}
         * \f$.
          */
        void OnObservationJacobians(
                const size_t& idx_landmark_to_predict, KFMatrix_OxV& Hx,
                KFMatrix_OxF& Hy) const;
 
        /** Computes A=A-B, which may need to be re-implemented depending on the
         * topology of the individual scalar components (eg, angles).
          */
        void OnSubstractObservationVectors(
                KFArray_OBS& A, const KFArray_OBS& B) const;
 
        /** Return the observation NOISE covariance matrix, that is, the model of
         * the Gaussian additive noise of the sensor.
          * \param out_R The noise covariance matrix. It might be non diagonal, but
         * it'll usually be.
          */
        void OnGetObservationNoise(KFMatrix_OxO& out_R) const;
 
        /** 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.
          * \param 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.
          * \param 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.
          * \sa OnGetObservations, OnDataAssociation
          */
        void OnPreComputingPredictions(
                const vector_KFArray_OBS& in_all_prediction_means,
                std::vector<size_t>& out_LM_indices_to_predict) const;
 
        /** If applicable to the given problem, this method implements the inverse
         * observation model needed to extend the "map" with a new "element".
          * \param in_z The observation vector whose inverse sensor model is to be
         * computed. This is actually one of the vector<> returned by
         * OnGetObservations().
          * \param out_yn The F-length vector with the inverse observation model \f$
         * y_n=y(x,z_n) \f$.
          * \param out_dyn_dxv The \f$F \times V\f$ Jacobian of the inv. sensor
         * model wrt the robot pose \f$ \frac{\partial y_n}{\partial x_v} \f$.
          * \param out_dyn_dhn The \f$F \times O\f$ Jacobian of the inv. sensor
         * model wrt the observation vector \f$ \frac{\partial y_n}{\partial h_n}
         * \f$.
          *
          *  - 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.
          */
        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, do here any special handling of
         * adding a new landmark to the map.
          * \param 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.
          * \param 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.
          * \sa OnInverseObservationModel
          */
        void OnNewLandmarkAddedToMap(
                const size_t in_obsIdx, const size_t in_idxNewFeat);
 
        /** 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.
          */
        void OnNormalizeStateVector();
 
        /** @}
         */
 
        /** Set up by processActionObservation */
        mrpt::obs::CActionCollection::Ptr m_action;
 
        /** Set up by processActionObservation */
        mrpt::obs::CSensoryFrame::Ptr m_SF;
 
        /** The mapping between landmark IDs and indexes in the Pkk cov. matrix: */
        mrpt::containers::bimap<mrpt::maps::CLandmark::TLandmarkID, unsigned int>
                m_IDs;
 
        /** Used for map partitioning experiments */
        CIncrementalMapPartitioner mapPartitioner;
 
        /** The sequence of all the observations and the robot path (kept for
         * debugging, statistics,etc)
          */
        mrpt::maps::CSimpleMap m_SFs;
 
        std::vector<std::vector<uint32_t>> m_lastPartitionSet;
 
        /** Last data association */
        TDataAssocInfo m_last_data_association;
 
        /** Return the last odometry, as a pose increment. */
        mrpt::poses::CPose3DQuat getIncrementFromOdometry() const;
 
};  // end class
}  // End of namespace
}  // End of namespace
 
#endif