maps/CRandomFieldGridMap2D.h

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2017, 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 CRandomFieldGridMap2D_H
#define CRandomFieldGridMap2D_H
 
#include <mrpt/utils/CDynamicGrid.h>
#include <mrpt/utils/CImage.h>
#include <mrpt/utils/CSerializable.h>
#include <mrpt/math/CMatrixD.h>
#include <mrpt/utils/CLoadableOptions.h>
#include <mrpt/utils/TEnumType.h>
#include <mrpt/maps/CMetricMap.h>
#include <mrpt/graphs/ScalarFactorGraph.h>
 
#include <mrpt/maps/link_pragmas.h>
#include <list>
 
namespace mrpt
{
namespace maps
{
        class COccupancyGridMap2D;
 
        DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE_LINKAGE( CRandomFieldGridMap2D , CMetricMap, MAPS_IMPEXP )
 
        // Pragma defined to ensure no structure packing: since we'll serialize TRandomFieldCell to streams, we want it not to depend on compiler options, etc.
#if defined(MRPT_IS_X86_AMD64)
#pragma pack(push,1)
#endif
 
        /** The contents of each cell in a CRandomFieldGridMap2D map.
          * \ingroup mrpt_maps_grp
         **/
        struct MAPS_IMPEXP TRandomFieldCell
        {
                /** Constructor */
                TRandomFieldCell(double kfmean_dm_mean = 1e-20, double kfstd_dmmeanw = 0) :
                        kf_mean      (kfmean_dm_mean),
                        kf_std       (kfstd_dmmeanw),
                        dmv_var_mean (0),
                        last_updated(mrpt::system::now()),
                        updated_std (kfstd_dmmeanw)
                { }
 
                // *Note*: Use unions to share memory between data fields, since only a set
                //          of the variables will be used for each mapping strategy.
                // You can access to a "TRandomFieldCell *cell" like: cell->kf_mean, cell->kf_std, etc..
                //  but accessing cell->kf_mean would also modify (i.e. ARE the same memory slot) cell->dm_mean, for example.
 
                // Note 2: If the number of type of fields are changed in the future,
                //   *PLEASE* also update the writeToStream() and readFromStream() methods!!
 
                union
                {
                        double kf_mean;         //!< [KF-methods only] The mean value of this cell
                        double dm_mean;         //!< [Kernel-methods only] The cumulative weighted readings of this cell
                        double gmrf_mean;       //!< [GMRF only] The mean value of this cell
                };
 
                union
                {
                        double kf_std;    //!< [KF-methods only] The standard deviation value of this cell
                        double dm_mean_w; //!< [Kernel-methods only] The cumulative weights (concentration = alpha * dm_mean / dm_mean_w + (1-alpha)*r0 )
                        double gmrf_std;
                };
 
                double dmv_var_mean;   //!< [Kernel DM-V only] The cumulative weighted variance of this cell
 
                mrpt::system::TTimeStamp last_updated;  //!< [Dynamic maps only] The timestamp of the last time the cell was updated
                double updated_std;                     //!< [Dynamic maps only] The std cell value that was updated (to be used in the Forgetting_curve
        };
 
#if defined(MRPT_IS_X86_AMD64)
#pragma pack(pop)
#endif
 
        /** CRandomFieldGridMap2D represents a 2D grid map where each cell is associated one real-valued property which is estimated by this map, either
          *   as a simple value or as a probility distribution (for each cell).
          *
          *  There are a number of methods available to build the MRF grid-map, depending on the value of
          *    `TMapRepresentation maptype` passed in the constructor.
          *
          *  The following papers describe the mapping alternatives implemented here:
          *             - `mrKernelDM`: A Gaussian kernel-based method. See:
          *                     - "Building gas concentration gridmaps with a mobile robot", Lilienthal, A. and Duckett, T., Robotics and Autonomous Systems, v.48, 2004.
          *             - `mrKernelDMV`: A kernel-based method. See:
          *                     - "A Statistical Approach to Gas Distribution Modelling with Mobile Robots--The Kernel DM+ V Algorithm", Lilienthal, A.J. and Reggente, M. and Trincavelli, M. and Blanco, J.L. and Gonzalez, J., IROS 2009.
          *             - `mrKalmanFilter`: A "brute-force" approach to estimate the entire map with a dense (linear) Kalman filter. Will be very slow for mid or large maps. It's provided just for comparison purposes, not useful in practice.
          *             - `mrKalmanApproximate`: A compressed/sparse Kalman filter approach. See:
          *                     - "A Kalman Filter Based Approach to Probabilistic Gas Distribution Mapping", JL Blanco, JG Monroy, J Gonzalez-Jimenez, A Lilienthal, 28th Symposium On Applied Computing (SAC), 2013.
          *             - `mrGMRF_SD`: A Gaussian Markov Random Field (GMRF) estimator, with these constraints:
          *                     - `mrGMRF_SD`: Each cell only connected to its 4 immediate neighbors (Up, down, left, right).
          *                     - (Removed in MRPT 1.5.0: `mrGMRF_G`: Each cell connected to a square area of neighbors cells)
          *                     - See papers:
          *                             - "Time-variant gas distribution mapping with obstacle information", Monroy, J. G., Blanco, J. L., & Gonzalez-Jimenez, J. Autonomous Robots, 40(1), 1-16, 2016.
          *
          *  Note that this class is virtual, since derived classes still have to implement:
          *             - mrpt::maps::CMetricMap::internal_computeObservationLikelihood()
          *             - mrpt::maps::CMetricMap::internal_insertObservation()
          *             - Serialization methods: writeToStream() and readFromStream()
          *
          * [GMRF only] A custom connectivity pattern between cells can be defined by calling setCellsConnectivity().
          *
          * \sa mrpt::maps::CGasConcentrationGridMap2D, mrpt::maps::CWirelessPowerGridMap2D, mrpt::maps::CMetricMap, mrpt::utils::CDynamicGrid, The application icp-slam, mrpt::maps::CMultiMetricMap
          * \ingroup mrpt_maps_grp
          */
        class CRandomFieldGridMap2D :
                public mrpt::maps::CMetricMap,
                public mrpt::utils::CDynamicGrid<TRandomFieldCell>,
                public mrpt::utils::COutputLogger
        {
                typedef utils::CDynamicGrid<TRandomFieldCell> BASE;
 
                // This must be added to any CSerializable derived class:
                DEFINE_VIRTUAL_SERIALIZABLE( CRandomFieldGridMap2D )
        public:
 
                /** Calls the base CMetricMap::clear
                  * Declared here to avoid ambiguity between the two clear() in both base classes.
                  */
                inline void clear() { CMetricMap::clear(); }
 
                // This method is just used for the ::saveToTextFile() method in base class.
                float cell2float(const TRandomFieldCell& c) const MRPT_OVERRIDE
                {
                        return c.kf_mean;
                }
 
                /** The type of map representation to be used, see CRandomFieldGridMap2D for a discussion.
                  */
                enum TMapRepresentation
                {
                        mrKernelDM = 0,  //!< Gaussian kernel-based estimator (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        mrAchim = 0,     //!< Another alias for "mrKernelDM", for backwards compatibility (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        mrKalmanFilter,  //!< "Brute-force" Kalman filter (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        mrKalmanApproximate, //!< (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        mrKernelDMV,     //!< Double mean + variance Gaussian kernel-based estimator (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        // Removed in MRPT 1.5.0: mrGMRF_G,   //!< Gaussian Markov Random Field, Gaussian prior weights between neighboring cells up to a certain distance (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                        mrGMRF_SD   //!< Gaussian Markov Random Field, squared differences prior weights between 4 neighboring cells (see discussion in mrpt::maps::CRandomFieldGridMap2D)
                };
 
                /** Constructor */
                CRandomFieldGridMap2D(
                        TMapRepresentation      mapType = mrKernelDM,
                        double x_min = -2, double x_max = 2,
                        double y_min = -2, double y_max = 2,
                        double resolution = 0.1
                        );
 
                /** Destructor */
                virtual ~CRandomFieldGridMap2D();
 
                 /** Returns true if the map is empty/no observation has been inserted (in this class it always return false,
                   * unless redefined otherwise in base classes)
                   */
                virtual bool isEmpty() const MRPT_OVERRIDE;
 
                /** Save the current map as a graphical file (BMP,PNG,...).
                  * The file format will be derived from the file extension (see  CImage::saveToFile )
                  *  It depends on the map representation model:
                  *             mrAchim: Each pixel is the ratio \f$ \sum{\frac{wR}{w}} \f$
                  *             mrKalmanFilter: Each pixel is the mean value of the Gaussian that represents each cell.
                  *
                  * \sa \a getAsBitmapFile()
                  */
                virtual void  saveAsBitmapFile(const std::string  &filName) const;
 
                /** Returns an image just as described in \a saveAsBitmapFile */
                virtual void  getAsBitmapFile(mrpt::utils::CImage &out_img) const;
 
                /** Like saveAsBitmapFile(), but returns the data in matrix form (first row in the matrix is the upper (y_max) part of the map) */
                virtual void getAsMatrix( mrpt::math::CMatrixDouble &out_mat) const;
 
                /** Parameters common to any derived class.
                  *  Derived classes should derive a new struct from this one, plus "public utils::CLoadableOptions",
                  *  and call the internal_* methods where appropiate to deal with the variables declared here.
                  *  Derived classes instantions of their "TInsertionOptions" MUST set the pointer "m_insertOptions_common" upon construction.
                  */
                struct MAPS_IMPEXP TInsertionOptionsCommon
                {
                        TInsertionOptionsCommon();      //!< Default values loader
 
                        /** See utils::CLoadableOptions */
                        void  internal_loadFromConfigFile_common(
                                const mrpt::utils::CConfigFileBase  &source,
                                const std::string &section);
 
                        void  internal_dumpToTextStream_common(mrpt::utils::CStream     &out) const; //!< See utils::CLoadableOptions
 
                        /** @name Kernel methods (mrKernelDM, mrKernelDMV)
                            @{ */
                        float   sigma;  //!< The sigma of the "Parzen"-kernel Gaussian
                        float   cutoffRadius;   //!< The cutoff radius for updating cells.
                        float   R_min,R_max;    //!< Limits for normalization of sensor readings.
                        double  dm_sigma_omega; //!< [DM/DM+V methods] The scaling parameter for the confidence "alpha" values (see the IROS 2009 paper; see CRandomFieldGridMap2D) */
                        /** @} */
 
                        /** @name Kalman-filter methods (mrKalmanFilter, mrKalmanApproximate)
                            @{ */
                        float   KF_covSigma;    //!< The "sigma" for the initial covariance value between cells (in meters).
                        float   KF_initialCellStd;      //!< The initial standard deviation of each cell's concentration (will be stored both at each cell's structure and in the covariance matrix as variances in the diagonal) (in normalized concentration units).
                        float   KF_observationModelNoise;       //!< The sensor model noise (in normalized concentration units).
                        float   KF_defaultCellMeanValue;        //!< The default value for the mean of cells' concentration.
                        uint16_t        KF_W_size;      //!< [mrKalmanApproximate] The size of the window of neighbor cells.
                        /** @} */
 
                        /** @name Gaussian Markov Random Fields methods (mrGMRF_SD)
                            @{ */
                        double GMRF_lambdaPrior;                //!< The information (Lambda) of fixed map constraints
                        double GMRF_lambdaObs;                  //!< The initial information (Lambda) of each observation (this information will decrease with time)
                        double GMRF_lambdaObsLoss;              //!< The loss of information of the observations with each iteration
 
                        bool GMRF_use_occupancy_information;    //!< whether to use information of an occupancy_gridmap map for building the GMRF
                        std::string GMRF_simplemap_file;                //!< simplemap_file name of the occupancy_gridmap
                        std::string GMRF_gridmap_image_file;    //!< image name of the occupancy_gridmap
                        double GMRF_gridmap_image_res;                  //!< occupancy_gridmap resolution: size of each pixel (m)
                        size_t GMRF_gridmap_image_cx;                   //!< Pixel coordinates of the origin for the occupancy_gridmap
                        size_t GMRF_gridmap_image_cy;                   //!< Pixel coordinates of the origin for the occupancy_gridmap
 
                        double    GMRF_saturate_min, GMRF_saturate_max; //!< (Default:-inf,+inf) Saturate the estimated mean in these limits
                        bool      GMRF_skip_variance;     //!< (Default:false) Skip the computation of the variance, just compute the mean
                        /** @} */
                };
 
                /** Changes the size of the grid, maintaining previous contents. \sa setSize */
                virtual void  resize(double new_x_min, double new_x_max, double new_y_min, double new_y_max, const TRandomFieldCell& defaultValueNewCells, double additionalMarginMeters = 1.0f ) MRPT_OVERRIDE;
 
                /** Changes the size of the grid, erasing previous contents.
                  *  \param[in] connectivity_descriptor Optional user-supplied object that will visit all grid cells to define their connectivity with neighbors and the strength of existing edges. If present, it overrides all options in insertionOptions
                  * \sa resize
                  */
                virtual void setSize(const double x_min, const double x_max, const double y_min, const double y_max, const double resolution, const TRandomFieldCell * fill_value = NULL);
 
                /** Base class for user-supplied objects capable of describing cells connectivity, used to build prior factors of the MRF graph. \sa setCellsConnectivity() */
                struct MAPS_IMPEXP ConnectivityDescriptor
                {
                        ConnectivityDescriptor();
                        virtual ~ConnectivityDescriptor();
 
                        /** Implement the check of whether node i=(icx,icy) is connected with node j=(jcx,jcy).
                          * This visitor method will be called only for immediate neighbors.
                          * \return true if connected (and the "information" value should be also updated in out_edge_information), false otherwise.
                          */
                        virtual bool getEdgeInformation(
                                const CRandomFieldGridMap2D *parent,  //!< The parent map on which we are running
                                size_t icx, size_t icy,               //!< (cx,cy) for node "i"
                                size_t jcx, size_t jcy,               //!< (cx,cy) for node "j"
                                double &out_edge_information          //!< Must output here the inverse of the variance of the constraint edge.
                        ) = 0;
                };
                typedef std::shared_ptr<ConnectivityDescriptor> ConnectivityDescriptorPtr;
 
                /** Sets a custom object to define the connectivity between cells. Must call clear() or setSize() afterwards for the changes to take place. */
                void setCellsConnectivity(const ConnectivityDescriptorPtr &new_connectivity_descriptor);
 
                /** See docs in base class: in this class this always returns 0 */
                float compute3DMatchingRatio(const mrpt::maps::CMetricMap *otherMap, const mrpt::poses::CPose3D &otherMapPose, const TMatchingRatioParams &params) const MRPT_OVERRIDE;
 
                /** The implementation in this class just calls all the corresponding method of the contained metric maps */
                virtual void  saveMetricMapRepresentationToFile(const std::string &filNamePrefix) const MRPT_OVERRIDE;
 
                /** Save a matlab ".m" file which represents as 3D surfaces the mean and a given confidence level for the concentration of each cell.
                  *  This method can only be called in a KF map model.
                  *  \sa getAsMatlab3DGraphScript    */
                virtual void  saveAsMatlab3DGraph(const std::string  &filName) const;
 
                /** Return a large text block with a MATLAB script to plot the contents of this map \sa saveAsMatlab3DGraph
                  *  This method can only be called in a KF map model  */
                void  getAsMatlab3DGraphScript(std::string  &out_script) const;
 
                /** Returns a 3D object representing the map (mean) */
                virtual void getAs3DObject( mrpt::opengl::CSetOfObjectsPtr &outObj ) const MRPT_OVERRIDE;
 
                /** Returns two 3D objects representing the mean and variance maps */
                virtual void  getAs3DObject ( mrpt::opengl::CSetOfObjectsPtr    &meanObj, mrpt::opengl::CSetOfObjectsPtr        &varObj ) const;
 
                TMapRepresentation       getMapType(); //!< Return the type of the random-field grid map, according to parameters passed on construction.
 
                /** Direct update of the map with a reading in a given position of the map, using
                  *  the appropriate method according to mapType passed in the constructor.
                  *
                  * This is a direct way to update the map, an alternative to the generic insertObservation() method which works with mrpt::obs::CObservation objects.
                  */
                void insertIndividualReading(
                        const double sensorReading,          //!< [in] The value observed in the (x,y) position
                        const mrpt::math::TPoint2D & point,  //!< [in] The (x,y) location
                        const bool update_map = true,        //!< [in] Run a global map update after inserting this observatin (algorithm-dependant)
                        const bool time_invariant = true,     //!< [in] Whether the observation "vanishes" with time (false) or not (true) [Only for GMRF methods]
                        const double reading_stddev = .0      //!< [in] The uncertainty (standard deviation) of the reading. Default="0.0" means use the default settings per map-wide parameters.
                        );
 
                enum TGridInterpolationMethod {
                        gimNearest = 0,
                        gimBilinear
                };
 
                /** Returns the prediction of the measurement at some (x,y) coordinates, and its certainty (in the form of the expected variance).  */
                virtual void predictMeasurement(
                        const double    x,                                 //!< [in] Query X coordinate
                        const double    y,                                 //!< [in] Query Y coordinate
                        double                  &out_predict_response,             //!< [out] The output value
                        double                  &out_predict_response_variance,    //!< [out] The output variance
                        bool                    do_sensor_normalization,           //!< [in] Whether to renormalize the prediction to a predefined interval (`R` values in insertionOptions)
                        const TGridInterpolationMethod interp_method = gimNearest //!< [in] Interpolation method
                        );
 
                /** Return the mean and covariance vector of the full Kalman filter estimate (works for all KF-based methods). */
                void getMeanAndCov( mrpt::math::CVectorDouble &out_means, mrpt::math::CMatrixDouble &out_cov) const;
 
                /** Return the mean and STD vectors of the full Kalman filter estimate (works for all KF-based methods). */
                void getMeanAndSTD( mrpt::math::CVectorDouble &out_means, mrpt::math::CVectorDouble &out_STD) const;
 
                /** Load the mean and STD vectors of the full Kalman filter estimate (works for all KF-based methods). */
                void setMeanAndSTD( mrpt::math::CVectorDouble &out_means, mrpt::math::CVectorDouble &out_STD);
 
                void updateMapEstimation(); //!< Run the method-specific procedure required to ensure that the mean & variances are up-to-date with all inserted observations.
 
                void enableVerbose(bool enable_verbose) { this->setMinLoggingLevel(mrpt::utils::LVL_DEBUG); }
                bool isEnabledVerbose() const { return this->getMinLoggingLevel()== mrpt::utils::LVL_DEBUG; }
 
                void enableProfiler(bool enable = true) { this->m_gmrf.enableProfiler(enable); }
                bool isProfilerEnabled() const { return this->m_gmrf.isProfilerEnabled() ; }
 
        protected:
                bool m_rfgm_run_update_upon_clear;
 
                /** Common options to all random-field grid maps: pointer that is set to the derived-class instance of "insertOptions" upon construction of this class. */
                TInsertionOptionsCommon * m_insertOptions_common;
 
                /** Get the part of the options common to all CRandomFieldGridMap2D classes */
                virtual CRandomFieldGridMap2D::TInsertionOptionsCommon* getCommonInsertOptions() = 0;
 
                TMapRepresentation      m_mapType; //!< The map representation type of this map, as passed in the constructor
 
                mrpt::math::CMatrixD                            m_cov;  //!< The whole covariance matrix, used for the Kalman Filter map representation.
 
                /** The compressed band diagonal matrix for the KF2 implementation.
                  *   The format is a Nx(W^2+2W+1) matrix, one row per cell in the grid map with the
                  *    cross-covariances between each cell and half of the window around it in the grid.
                  */
                mrpt::math::CMatrixD            m_stackedCov;
                mutable bool    m_hasToRecoverMeanAndCov;       //!< Only for the KF2 implementation.
 
                /** @name Auxiliary vars for DM & DM+V methods
                    @{ */
                float               m_DM_lastCutOff;
                std::vector<float>      m_DM_gaussWindow;
                double                          m_average_normreadings_mean, m_average_normreadings_var;
                size_t              m_average_normreadings_count;
                /** @} */
 
                ConnectivityDescriptorPtr m_gmrf_connectivity; //!< Empty: default
 
                mrpt::graphs::ScalarFactorGraph  m_gmrf;
 
                struct TObservationGMRF : public mrpt::graphs::ScalarFactorGraph::UnaryFactorVirtualBase
                {
                        double obsValue;       //!< Observation value
                        double Lambda;         //!< "Information" of the observation (=inverse of the variance)
                        bool   time_invariant; //!< whether the observation will lose weight (lambda) as time goes on (default false)
 
                        double evaluateResidual() const MRPT_OVERRIDE;
                        double getInformation() const  MRPT_OVERRIDE;
                        void evalJacobian(double &dr_dx) const MRPT_OVERRIDE;
 
                        TObservationGMRF( CRandomFieldGridMap2D &parent ) : obsValue(.0), Lambda(.0), time_invariant(false), m_parent(&parent) {}
                private:
                        CRandomFieldGridMap2D *m_parent;
                };
 
                struct TPriorFactorGMRF : public mrpt::graphs::ScalarFactorGraph::BinaryFactorVirtualBase
                {
                        double Lambda;         //!< "Information" of the observation (=inverse of the variance)
 
                        double evaluateResidual() const MRPT_OVERRIDE;
                        double getInformation() const MRPT_OVERRIDE;
                        void evalJacobian(double &dr_dx_i, double &dr_dx_j) const MRPT_OVERRIDE;
 
                        TPriorFactorGMRF(CRandomFieldGridMap2D &parent) : Lambda(.0), m_parent(&parent) {}
                private:
                        CRandomFieldGridMap2D *m_parent;
                };
 
                // Important: converted to a std::list<> so pointers are NOT invalidated upon deletion.
                std::vector<std::list<TObservationGMRF> > m_mrf_factors_activeObs; //!< Vector with the active observations and their respective Information
                std::deque<TPriorFactorGMRF>               m_mrf_factors_priors; //!< Vector with the precomputed priors for each GMRF model
 
                /** The implementation of "insertObservation" for Achim Lilienthal's map models DM & DM+V.
                  * \param normReading Is a [0,1] normalized concentration reading.
                  * \param point Is the sensor location on the map
                  * \param is_DMV = false -> map type is Kernel DM; true -> map type is DM+V
                  */
                void  insertObservation_KernelDM_DMV(
                        double  normReading,
                        const mrpt::math::TPoint2D &point,
                        bool             is_DMV );
 
                /** The implementation of "insertObservation" for the (whole) Kalman Filter map model.
                  * \param normReading Is a [0,1] normalized concentration reading.
                  * \param point Is the sensor location on the map
                  */
                void  insertObservation_KF(
                        double  normReading,
                        const mrpt::math::TPoint2D &point );
 
                /** The implementation of "insertObservation" for the Efficient Kalman Filter map model.
                  * \param normReading Is a [0,1] normalized concentration reading.
                  * \param point Is the sensor location on the map
                  */
                void  insertObservation_KF2(
                        double normReading,
                        const mrpt::math::TPoint2D &point );
 
                /** The implementation of "insertObservation" for the Gaussian Markov Random Field map model.
                  * \param normReading Is a [0,1] normalized concentration reading.
                  * \param point Is the sensor location on the map
                  */
                void  insertObservation_GMRF(double normReading,const mrpt::math::TPoint2D &point, const bool update_map,const bool time_invariant, const double reading_information);
 
                /** solves the minimum quadratic system to determine the new concentration of each cell */
                void  updateMapEstimation_GMRF();
 
                /** Computes the confidence of the cell concentration (alpha) */
                double computeConfidenceCellValue_DM_DMV (const TRandomFieldCell *cell ) const;
 
                /** Computes the average cell concentration, or the overall average value if it has never been observed  */
                double computeMeanCellValue_DM_DMV (const TRandomFieldCell *cell ) const;
 
                /** Computes the estimated variance of the cell concentration, or the overall average variance if it has never been observed  */
                double computeVarCellValue_DM_DMV (const TRandomFieldCell *cell ) const;
 
                /** In the KF2 implementation, takes the auxiliary matrices and from them update the cells' mean and std values.
                  * \sa m_hasToRecoverMeanAndCov
                  */
                void  recoverMeanAndCov() const;
 
                /** Erase all the contents of the map */
                virtual void  internal_clear() MRPT_OVERRIDE;
 
                /** Check if two cells of the gridmap (m_map) are connected, based on the provided occupancy gridmap*/
                bool exist_relation_between2cells(
                        const mrpt::maps::COccupancyGridMap2D *m_Ocgridmap,
                        size_t cxo_min,
                        size_t cxo_max,
                        size_t cyo_min,
                        size_t cyo_max,
                        const size_t seed_cxo,
                        const size_t seed_cyo,
                        const size_t objective_cxo,
                        const size_t objective_cyo);
        };
        DEFINE_SERIALIZABLE_POST_CUSTOM_BASE_LINKAGE( CRandomFieldGridMap2D , CMetricMap, MAPS_IMPEXP )
 
 
        } // End of namespace
 
 
        // Specializations MUST occur at the same namespace:
        namespace utils
        {
                template <>
                struct TEnumTypeFiller<maps::CRandomFieldGridMap2D::TMapRepresentation>
                {
                        typedef maps::CRandomFieldGridMap2D::TMapRepresentation enum_t;
                        static void fill(bimap<enum_t,std::string>  &m_map)
                        {
                                m_map.insert(maps::CRandomFieldGridMap2D::mrKernelDM,          "mrKernelDM");
                                m_map.insert(maps::CRandomFieldGridMap2D::mrKalmanFilter,      "mrKalmanFilter");
                                m_map.insert(maps::CRandomFieldGridMap2D::mrKalmanApproximate, "mrKalmanApproximate");
                                m_map.insert(maps::CRandomFieldGridMap2D::mrKernelDMV,         "mrKernelDMV");
                                m_map.insert(maps::CRandomFieldGridMap2D::mrGMRF_SD,               "mrGMRF_SD");
                        }
                };
        } // End of namespace
} // End of namespace
 
#endif