CLandmarksMap.h

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2014, 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 CLandmarksMap_H
#define CLandmarksMap_H

#include <mrpt/vision/CFeatureExtraction.h>
#include <mrpt/slam/CMetricMap.h>
#include <mrpt/slam/CLandmark.h>
#include <mrpt/slam/CObservationImage.h>
#include <mrpt/slam/CObservation2DRangeScan.h>
#include <mrpt/slam/CObservationGPS.h>
#include <mrpt/slam/CObservationBearingRange.h>
#include <mrpt/utils/CSerializable.h>
#include <mrpt/math/CMatrix.h>
#include <mrpt/utils/CDynamicGrid.h>
#include <mrpt/utils/CLoadableOptions.h>

namespace mrpt
{
namespace slam
{
        class CObservationBeaconRanges;
        class CObservationStereoImages;

        /** Internal use.
          */
        typedef std::vector<CLandmark>  TSequenceLandmarks;

        DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE_LINKAGE( CLandmarksMap, CMetricMap, VISION_IMPEXP )

        /** A class for storing a map of 3D probabilistic landmarks.
         * <br>
         *  Currently these types of landmarks are defined: (see mrpt::slam::CLandmark)
         *              - For "visual landmarks" from images: features with associated descriptors.
         *              - For laser scanners: each of the range measuremnts, as "occupancy" landmarks.
         *              - For grid maps: "Panoramic descriptor" feature points.
         *              - For range-only localization and SLAM: Beacons. It is also supported the simulation of expected beacon-to-sensor readings, observation likelihood,...
         * <br>
         * <b>How to load landmarks from observations:</b><br>
         *  When invoking CLandmarksMap::insertObservation(), the values in CLandmarksMap::insertionOptions will
         *     determinate the kind of landmarks that will be extracted and fused into the map. Supported feature
         *     extraction processes are listed next:
         *
          <table>
          <tr> <td><b>Observation class:</b></td> <td><b>Generated Landmarks:</b></td> <td><b>Comments:</b></td> </tr>
          <tr> <td>CObservationImage</td> <td>vlSIFT</td> <td>1) A SIFT feature is created for each SIFT detected in the image,
                   <br>2) Correspondences between SIFTs features and existing ones are finded by computeMatchingWith3DLandmarks,
                           <br>3) The corresponding feaures are fused, and the new ones added, with an initial uncertainty according to insertionOptions</td> </tr>
          <tr> <td>CObservationStereoImages</td> <td>vlSIFT</td> <td> Each image is separately procesed by the method for CObservationImage observations </td> </tr>
          <tr> <td>CObservationStereoImages</td> <td>vlColor</td> <td> TODO... </td> </tr>
          <tr> <td>CObservation2DRangeScan</td> <td>glOccupancy</td> <td> A landmark is added for each range in the scan, with its appropiate covariance matrix derived from the jacobians matrixes. </td> </tr>
          </table>
         *
         * \sa CMetricMap
         * \ingroup mrpt_vision_grp
         */
        class VISION_IMPEXP CLandmarksMap : public CMetricMap
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CLandmarksMap )

        private:

                virtual void    internal_clear();

                 /** Insert the observation information into this map. This method must be implemented
                  *    in derived classes.
                  * \param obs The observation
                  * \param robotPose The 3D pose of the robot mobile base in the map reference system, or NULL (default) if you want to use CPose2D(0,0,deg)
                  *
                  * \sa CObservation::insertObservationInto
                  */
                virtual bool  internal_insertObservation( const CObservation *obs, const CPose3D *robotPose = NULL );

        public:

                static mrpt::utils::TColorf             COLOR_LANDMARKS_IN_3DSCENES;  //!< The color of landmark ellipsoids in CLandmarksMap::getAs3DObject

                typedef mrpt::slam::CLandmark  landmark_type;


                /** The list of landmarks: the wrapper class is just for maintaining the KD-Tree representation
                  */
                struct VISION_IMPEXP TCustomSequenceLandmarks
                {
                private:
                        /** The actual list:
                          */
                        TSequenceLandmarks                      m_landmarks;

                        /** A grid-map with the set of landmarks falling into each cell.
                      *  \todo Use the KD-tree instead?
                          */
                        CDynamicGrid<vector_int>        m_grid;

                        /** Auxiliary variables used in "getLargestDistanceFromOrigin"
                          * \sa getLargestDistanceFromOrigin
                          */
                        mutable float   m_largestDistanceFromOrigin;

                        /** Auxiliary variables used in "getLargestDistanceFromOrigin"
                          * \sa getLargestDistanceFromOrigin
                          */
                        mutable bool    m_largestDistanceFromOriginIsUpdated;

                public:
                        /** Default constructor
                          */
                        TCustomSequenceLandmarks();

                        typedef TSequenceLandmarks::iterator    iterator;
                        inline iterator                         begin()                 { return m_landmarks.begin(); };
                        inline iterator                         end()                   { return m_landmarks.end(); };
                        void clear();
                        inline size_t   size()  const   { return m_landmarks.size(); };

                        typedef TSequenceLandmarks::const_iterator      const_iterator;
                        inline const_iterator                   begin() const   { return m_landmarks.begin(); };
                        inline const_iterator                   end()   const   { return m_landmarks.end(); };

                        /** The object is copied, thus the original copy passed as a parameter can be released.
                          */
                        void                    push_back( const CLandmark &lm);
                        CLandmark*          get(unsigned int indx);
                        const CLandmark* get(unsigned int indx) const;
                        void                    isToBeModified(unsigned int indx);
                        void                    hasBeenModified(unsigned int indx);
                        void                    hasBeenModifiedAll();
                        void                    erase(unsigned int indx);

                        CDynamicGrid<vector_int>*  getGrid() { return &m_grid; }

                        /** Returns the landmark with a given landmrk ID, or NULL if not found
                          */
                        const CLandmark*        getByID( CLandmark::TLandmarkID ID ) const;

                        /** Returns the landmark with a given beacon ID, or NULL if not found
                          */
                        const CLandmark*        getByBeaconID( unsigned int ID ) const;

                        /** This method returns the largest distance from the origin to any of the points, such as a sphere centered at the origin with this radius cover ALL the points in the map (the results are buffered, such as, if the map is not modified, the second call will be much faster than the first one).
                          */
                        float  getLargestDistanceFromOrigin() const;

                } landmarks;

                 /** Constructor
                   */
                 CLandmarksMap();

                 /** Virtual destructor.
                   */
                 virtual ~CLandmarksMap();


                 /**** FAMD ***/
                 /** Map of the Euclidean Distance between the descriptors of two SIFT-based landmarks
                  */
                 static std::map<std::pair<mrpt::slam::CLandmark::TLandmarkID, mrpt::slam::CLandmark::TLandmarkID>, double> _mEDD;
                 static mrpt::slam::CLandmark::TLandmarkID _mapMaxID;
                 static bool _maxIDUpdated;

                 mrpt::slam::CLandmark::TLandmarkID  getMapMaxID();
                 /**** END FAMD *****/

                // See docs in base class
                float  compute3DMatchingRatio(
                                const CMetricMap                                                *otherMap,
                                const CPose3D                                                   &otherMapPose,
                                float                                                                   maxDistForCorr = 0.10f,
                                float                                                                   maxMahaDistForCorr = 2.0f
                                ) const;

                 /** With this struct options are provided to the observation insertion process.
                  */
                 struct VISION_IMPEXP TInsertionOptions : public utils::CLoadableOptions
                 {
                 public:
                        /** Initilization of default parameters
                         */
                        TInsertionOptions(      );

                        /** See utils::CLoadableOptions
                          */
                        void  loadFromConfigFile(
                                const mrpt::utils::CConfigFileBase      &source,
                                const std::string &section);

                        /** See utils::CLoadableOptions
                          */
                        void  dumpToTextStream(CStream  &out) const;

                        /** If set to true (default), the insertion of a CObservationImage in the map will insert SIFT 3D features.
                          */
                        bool    insert_SIFTs_from_monocular_images;

                        /** If set to true (default), the insertion of a CObservationStereoImages in the map will insert SIFT 3D features.
                          */
                        bool    insert_SIFTs_from_stereo_images;

                        /** If set to true (default), inserting a CObservation2DRangeScan in the map will generate landmarks for each range.
                          */
                        bool    insert_Landmarks_from_range_scans;

                        /** [For SIFT landmarks only] The ratio between the best and second best descriptor distances to set as correspondence (Default=0.4)
                          */
                        float   SiftCorrRatioThreshold;

                        /** [For SIFT landmarks only] The minimum likelihood value of a match to set as correspondence (Default=0.5)
                          */
                        float   SiftLikelihoodThreshold;

                        /****************************************** FAMD ******************************************/
                        /** [For SIFT landmarks only] The minimum Euclidean Descriptor Distance value of a match to set as correspondence (Default=200)
                          */
                        float   SiftEDDThreshold;

                        /** [For SIFT landmarks only] Method to compute 3D matching (Default = 0 (Our method))
                          * 0: Our method -> Euclidean Distance between Descriptors and 3D position
                          * 1: Sim, Elinas, Griffin, Little -> Euclidean Distance between Descriptors
                          */
                        unsigned int SIFTMatching3DMethod;

                        /** [For SIFT landmarks only] Method to compute the likelihood (Default = 0 (Our method))
                          * 0: Our method -> Euclidean Distance between Descriptors and 3D position
                          * 1: Sim, Elinas, Griffin, Little -> 3D position
                          */
                        unsigned int SIFTLikelihoodMethod;

                        /****************************************** END FAMD ******************************************/

                        /** [For SIFT landmarks only] The distance (in meters) of the mean value of landmarks, for the initial position PDF (Default = 3m)
                          */
                        float   SIFTsLoadDistanceOfTheMean;

                        /** [For SIFT landmarks only] The width (in meters, standard deviation) of the ellipsoid in the axis perpendicular to the main directiom (Default = 0.05f)
                          */
                        float   SIFTsLoadEllipsoidWidth;

                        /** [For SIFT landmarks only] The standard deviation (in pixels) for the SIFTs detector (This is used for the Jacobbian to project stereo images to 3D)
                          */
                        float   SIFTs_stdXY, SIFTs_stdDisparity;

                        /** Number of points to extract in the image
                          */
                        int             SIFTs_numberOfKLTKeypoints;

                        /** Maximum depth of 3D landmarks when loading a landmarks map from a stereo image observation.
                          */
                        float   SIFTs_stereo_maxDepth;

                        /** Maximum distance (in pixels) from a point to a certain epipolar line to be considered a potential match.
                          */
                        float   SIFTs_epipolar_TH;

                        /** Indicates if the images (as well as the SIFT detected features) should be shown in a window.
                          */
                        bool    PLOT_IMAGES;

                        /** Parameters of the SIFT feature detector/descriptors while inserting images in the landmark map.
                          *  \note There exists another \a SIFT_feat_options field in the \a likelihoodOptions member.
                          *  \note All parameters of this field can be loaded from a config file. See mrpt::vision::CFeatureExtraction::TOptions for the names of the expected fields.
                          */
                        mrpt::vision::CFeatureExtraction::TOptions  SIFT_feat_options;

                 } insertionOptions;

                 /** With this struct options are provided to the likelihood computations.
                  */
                 struct VISION_IMPEXP  TLikelihoodOptions : public utils::CLoadableOptions
                 {
                 public:
                        /** Initilization of default parameters
                         */
                         TLikelihoodOptions();

                        /** See utils::CLoadableOptions
                          */
                        void  loadFromConfigFile(
                                const mrpt::utils::CConfigFileBase      &source,
                                const std::string &section);

                        /** See utils::CLoadableOptions
                          */
                        void  dumpToTextStream(CStream  &out) const;

                         /** The number of rays from a 2D range scan will be decimated by this factor (default = 1, no decimation)
                           */
                         unsigned int   rangeScan2D_decimation;

                         double                 SIFTs_sigma_euclidean_dist;

                         double                 SIFTs_sigma_descriptor_dist;

                         float                  SIFTs_mahaDist_std;

                         float                  SIFTnullCorrespondenceDistance;

                         /** Considers 1 out of "SIFTs_decimation" visual landmarks in the observation during the likelihood computation.
                           */
                         int                    SIFTs_decimation;

                         /** The standard deviation used for Beacon ranges likelihood (default=0.08).
                           */
                         float                  beaconRangesStd;

                         /** The ratio between gaussian and uniform distribution (default=1).
                           */
                         float                  alphaRatio;

                         /** Maximun reliable beacon range value (default=20)
                           */
                         float                  beaconMaxRange;

                        /** This struct store de GPS longitude, latitude (in degrees ) and altitude (in meters) for the first GPS observation
                          * compose with de sensor position on the robot.
                         */
                        struct VISION_IMPEXP TGPSOrigin
                        {
                        public:
                                TGPSOrigin();

                                /** Longitud del Origen del GPS (en grados)
                                  */
                                double  longitude;

                                /** Latitud del Origen del GPS (en grados)
                                  */
                                double  latitude;

                                /** Altitud del Origen del GPS (en metros)
                                  */
                                double  altitude;

                                /** Estas tres opciones sirven para encajar mapas de GPS con posiciones absolutas con
                                  *  mapas de otros sensores (como laser :D) se obtienen facilmente con el programa matlab  map_matching
                                  *   ang : Rotación del mapa del GPS (para encajarlo en grados)
                                  *   x_shift: Desplazamiento en x relativo al robot (en metros)
                                  *   y_shift: Desplazamiento en y relativo al robot (en metros)
                              */
                                double  ang,
                                                x_shift,
                                                y_shift;

                                /** Número mínimo de satelites para tener en cuenta los datos
                                  */
                                unsigned int min_sat;

                        } GPSOrigin;

                        /** A constant "sigma" for GPS localization data (in meters)
                          */
                        float                   GPS_sigma;

                        /** Parameters of the SIFT feature detector/descriptors while inserting images in the landmark map.
                          *  \note There exists another \a SIFT_feat_options field in the \a insertionOptions member.
                          *  \note All parameters of this field can be loaded from a config file. See mrpt::vision::CFeatureExtraction::TOptions for the names of the expected fields.
                          */
                        mrpt::vision::CFeatureExtraction::TOptions  SIFT_feat_options;

                 } likelihoodOptions;

                 /** This struct stores extra results from invoking insertObservation
                  */
                 struct VISION_IMPEXP TInsertionResults
                 {
                         /** The number of SIFT detected in left and right images respectively
                           */

                         unsigned int   nSiftL, nSiftR;


                 } insertionResults;

                 /** With this struct options are provided to the fusion process.
                  */
                 struct VISION_IMPEXP TFuseOptions
                 {
                         /** Initialization
                           */
                         TFuseOptions();

                         /** Required number of times of a landmark to be seen not to be removed, in "ellapsedTime" seconds.
                           */
                         unsigned int   minTimesSeen;

                         /** See "minTimesSeen"
                           */
                         float                  ellapsedTime;

                 } fuseOptions;


                /** Save to a text file.
                 *  In line "i" there are the (x,y,z) mean values of the i'th landmark + type of landmark + # times seen + timestamp + RGB/descriptor + ID
                 *
                 *   Returns false if any error occured, true elsewere.
                 */
                bool  saveToTextFile(std::string file);

                /** Save to a MATLAB script which displays 2D error ellipses for the map (top-view, projection on the XY plane).
                 *      \param file             The name of the file to save the script to.
                 *  \param style        The MATLAB-like string for the style of the lines (see 'help plot' in MATLAB for posibilities)
                 *  \param stdCount The ellipsoids will be drawn from the center to "stdCount" times the "standard deviations". (default is 2std = 95% confidence intervals)
                 *
                 *  \return Returns false if any error occured, true elsewere.
                 */
                bool  saveToMATLABScript2D(
                        std::string             file,
                        const char                      *style="b",
                        float                   stdCount = 2.0f );

                /** Save to a MATLAB script which displays 3D error ellipses for the map.
                 *      \param file             The name of the file to save the script to.
                 *  \param style        The MATLAB-like string for the style of the lines (see 'help plot' in MATLAB for posibilities)
                 *  \param stdCount The ellipsoids will be drawn from the center to a given confidence interval in [0,1], e.g. 2 sigmas=0.95 (default is 2std = 0.95 confidence intervals)
                 *
                 *  \return Returns false if any error occured, true elsewere.
                 */
                bool  saveToMATLABScript3D(
                        std::string             file,
                        const char                      *style="b",
                        float                   confInterval = 0.95f ) const ;

                /** Returns the stored landmarks count.
                 */
                size_t  size() const;

                /** Computes the (logarithmic) likelihood function for a sensed observation "o" according to "this" map.
                  *   This is the implementation of the algorithm reported in the paper:
                                <em>J.L. Blanco, J. Gonzalez, and J.A. Fernandez-Madrigal, "A Consensus-based Approach for Estimating the Observation Likelihood of Accurate Range Sensors", in IEEE International Conference on Robotics and Automation (ICRA), Rome (Italy), Apr 10-14, 2007</em>
                  */
                double  computeLikelihood_RSLC_2007( const CLandmarksMap  *s, const CPose2D &sensorPose);

                /** Loads into this landmarks map the SIFT features extracted from an image observation (Previous contents of map will be erased)
                  *  The robot is assumed to be at the origin of the map.
                  *  Some options may be applicable from "insertionOptions" (insertionOptions.SIFTsLoadDistanceOfTheMean)
                  * 
                  *  \param feat_options Optionally, you can pass here parameters for changing the default SIFT detector settings.
                  */
                void  loadSiftFeaturesFromImageObservation( 
                        const CObservationImage &obs,
                        const mrpt::vision::CFeatureExtraction::TOptions & feat_options = mrpt::vision::CFeatureExtraction::TOptions(mrpt::vision::featSIFT)
                        );

                /** Loads into this landmarks map the SIFT features extracted from an observation consisting of a pair of stereo-image (Previous contents of map will be erased)
                  *  The robot is assumed to be at the origin of the map.
                  *  Some options may be applicable from "insertionOptions"
                  *
                  *  \param feat_options Optionally, you can pass here parameters for changing the default SIFT detector settings.
                  */
                void  loadSiftFeaturesFromStereoImageObservation( 
                        const CObservationStereoImages  &obs, 
                        mrpt::slam::CLandmark::TLandmarkID fID,
                        const mrpt::vision::CFeatureExtraction::TOptions & feat_options = mrpt::vision::CFeatureExtraction::TOptions(mrpt::vision::featSIFT)
                        );

                /** Loads into this landmarks-map a set of occupancy features according to a 2D range scan (Previous contents of map will be erased)
                  *  \param obs The observation
                  *  \param robotPose   The robot pose in the map (Default = the origin)
                  *  Some options may be applicable from "insertionOptions"
                  */
                void  loadOccupancyFeaturesFrom2DRangeScan(
                                        const CObservation2DRangeScan   &obs,
                                        const CPose3D                                   *robotPose = NULL,
                                        unsigned int                            downSampleFactor = 1);


                /** Computes the (logarithmic) likelihood that a given observation was taken from a given pose in the world being modeled with this map.
                 *
                 *  In the current implementation, this method behaves in a different way according to the nature of
                 *   the observation's class:
                 *              - "mrpt::slam::CObservation2DRangeScan": This calls "computeLikelihood_RSLC_2007".
                 *              - "mrpt::slam::CObservationStereoImages": This calls "computeLikelihood_SIFT_LandmarkMap".
                 *
                 * \param takenFrom The robot's pose the observation is supposed to be taken from.
                 * \param obs The observation.
                 * \return This method returns a likelihood value > 0.
                 *
                 * \sa Used in particle filter algorithms, see: CMultiMetricMapPDF::update
                 */
                double   computeObservationLikelihood( const CObservation *obs, const CPose3D &takenFrom );

                // See docs in base class
                void  computeMatchingWith2D(
                                const CMetricMap                                                                *otherMap,
                                const CPose2D                                                                   &otherMapPose,
                                float                                                                   maxDistForCorrespondence,
                                float                                                                   maxAngularDistForCorrespondence,
                                const CPose2D                                                                   &angularDistPivotPoint,
                                TMatchingPairList                                               &correspondences,
                                float                                                                   &correspondencesRatio,
                                float                                                                   *sumSqrDist     = NULL,
                                bool                                                                    onlyKeepTheClosest = false,
                                bool                                                                    onlyUniqueRobust = false ) const;

                /** Perform a search for correspondences between "this" and another lansmarks map:
                  *  In this class, the matching is established solely based on the landmarks' IDs.
                  * \param otherMap [IN] The other map.
                  * \param correspondences [OUT] The matched pairs between maps.
                  * \param correspondencesRatio [OUT] This is NumberOfMatchings / NumberOfLandmarksInTheAnotherMap
                  * \param otherCorrespondences [OUT] Will be returned with a vector containing "true" for the indexes of the other map's landmarks with a correspondence.
                  */
                void  computeMatchingWith3DLandmarks(
                                const mrpt::slam::CLandmarksMap                         *otherMap,
                                TMatchingPairList                                               &correspondences,
                                float                                                                   &correspondencesRatio,
                                std::vector<bool>                                               &otherCorrespondences) const;

                /** Changes the reference system of the map to a given 3D pose.
                  */
                void  changeCoordinatesReference( const CPose3D &newOrg );

                /** Changes the reference system of the map "otherMap" and save the result in "this" map.
                  */
                void  changeCoordinatesReference( const CPose3D &newOrg, const mrpt::slam::CLandmarksMap *otherMap );

                /** Fuses the contents of another map with this one, updating "this" object with the result.
                  *  This process involves fusing corresponding landmarks, then adding the new ones.
                  *  \param other The other landmarkmap, whose landmarks will be inserted into "this"
                  *  \param justInsertAllOfThem If set to "true", all the landmarks in "other" will be inserted into "this" without checking for possible correspondences (may appear duplicates ones, etc...)
                  */
                void  fuseWith( CLandmarksMap &other, bool justInsertAllOfThem = false );

                /** Returns the (logarithmic) likelihood of a set of landmarks "map" given "this" map.
                  *  See paper: JJAA 2006
                  */
                double   computeLikelihood_SIFT_LandmarkMap( CLandmarksMap *map,
                                                                                                                                          TMatchingPairList     *correspondences = NULL,
                                                                                                                                          std::vector<bool> *otherCorrespondences = NULL);

                /** Returns true if the map is empty/no observation has been inserted.
                   */
                bool  isEmpty() const;

                /** Simulates a noisy reading toward each of the beacons in the landmarks map, if any.
                  * \param in_robotPose This robot pose is used to simulate the ranges to each beacon.
                  * \param in_sensorLocationOnRobot The 3D position of the sensor on the robot
                  * \param out_Observations The results will be stored here. NOTICE that the fields "CObservationBeaconRanges::minSensorDistance","CObservationBeaconRanges::maxSensorDistance" and "CObservationBeaconRanges::stdError" MUST BE FILLED OUT before calling this function.
                  * An observation will be generated for each beacon in the map, but notice that some of them may be missed if out of the sensor maximum range.
                  */
                void  simulateBeaconReadings(
                        const CPose3D                                   &in_robotPose,
                        const CPoint3D                                  &in_sensorLocationOnRobot,
                        CObservationBeaconRanges                &out_Observations ) const;

                /** Simulates a noisy bearing-range observation of all the beacons (landamrks with type glBeacon) in the landmarks map, if any.
                  * \param[in]  robotPose The robot pose.
                  * \param[in]  sensorLocationOnRobot The 3D position of the sensor on the robot
                  * \param[out] observations The results will be stored here.
                  * \param[in]  sensorDetectsIDs If this is set to false, all the landmarks will be sensed with INVALID_LANDMARK_ID as ID.
                  * \param[in]  stdRange The sigma of the sensor noise in range (meters).
                  * \param[in]  stdYaw The sigma of the sensor noise in yaw (radians).
                  * \param[in]  stdPitch The sigma of the sensor noise in pitch (radians).
                  * \param[out] real_associations If it's not a NULL pointer, this will contain at the return the real indices of the landmarks in the map in the same order than they appear in out_Observations. Useful when sensorDetectsIDs=false. Spurious readings are assigned a std::string::npos (=-1) index.
                  * \param[in]  spurious_count_mean The mean number of spurious measurements (uniformly distributed in range & angle) to generate. The number of spurious is generated by rounding a random Gaussin number. If both this mean and the std are zero (the default) no spurious readings are generated.
                  * \param[in]  spurious_count_std  Read spurious_count_mean above.
                  *
                  * \note The fields "CObservationBearingRange::fieldOfView_*","CObservationBearingRange::maxSensorDistance" and "CObservationBearingRange::minSensorDistance" MUST BE FILLED OUT before calling this function.
                  * \note At output, the observation will have CObservationBearingRange::validCovariances set to "false" and the 3 sensor_std_* members correctly set to their values.
                  * An observation will be generated for each beacon in the map, but notice that some of them may be missed if out of the sensor maximum range or field of view-
                  */
                void  simulateRangeBearingReadings(
                        const CPose3D                                   &robotPose,
                        const CPose3D                                   &sensorLocationOnRobot,
                        CObservationBearingRange                &observations,
                        bool                            sensorDetectsIDs = true,
                        const float                     stdRange = 0.01f,
                        const float                     stdYaw = DEG2RAD(0.1f),
                        const float                     stdPitch = DEG2RAD(0.1f),
                        vector_size_t                                   *real_associations = NULL,
                        const double                    spurious_count_mean = 0,
                        const double                    spurious_count_std  = 0
                         ) const;


                /** This virtual method saves the map to a file "filNamePrefix"+< some_file_extension >, as an image or in any other applicable way (Notice that other methods to save the map may be implemented in classes implementing this virtual interface).
                  *  In the case of this class, these files are generated:
                  *             - "filNamePrefix"+"_3D.m": A script for MATLAB for drawing landmarks as 3D ellipses.
                  *             - "filNamePrefix"+"_3D.3DScene": A 3D scene with a "ground plane grid" and the set of ellipsoids in 3D.
                  */
                void  saveMetricMapRepresentationToFile(
                        const std::string       &filNamePrefix ) const;

                /** Returns a 3D object representing the map.
                  * \sa COLOR_LANDMARKS_IN_3DSCENES
                  */
                void  getAs3DObject ( mrpt::opengl::CSetOfObjectsPtr    &outObj ) const;

                /** This method is called at the end of each "prediction-update-map insertion" cycle within "mrpt::slam::CMetricMapBuilderRBPF::processActionObservation".
                  *  This method should normally do nothing, but in some cases can be used to free auxiliary cached variables.
                  */
                virtual void  auxParticleFilterCleanUp();

        }; // End of class def.
        DEFINE_SERIALIZABLE_POST_CUSTOM_BASE_LINKAGE( CLandmarksMap, CMetricMap, VISION_IMPEXP )


        } // End of namespace
} // End of namespace

#endif