CBeacon.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 CBeacon_H
#define CBeacon_H
 
#include <mrpt/serialization/CSerializable.h>
#include <mrpt/math/CMatrix.h>
#include <mrpt/poses/CPoint3D.h>
#include <mrpt/poses/CPointPDFParticles.h>
#include <mrpt/poses/CPointPDFGaussian.h>
#include <mrpt/poses/CPointPDFSOG.h>
#include <mrpt/obs/CObservation.h>  // INVALID_BEACON_ID
 
#include <mrpt/opengl/CSetOfObjects.h>
 
namespace mrpt
{
namespace maps
{
class CBeaconMap;
 
/** The class for storing individual "beacon landmarks" under a variety of 3D
 * position PDF distributions.
 *  This class is used for storage within the class CBeaconMap.
 *  The class implements the same methods than the interface "CPointPDF", and
 * invoking them actually becomes
 *   a mapping into the methods of the current PDF representation of the
 * beacon, selectable by means of "m_typePDF"
 * \sa CBeaconMap, CPointPDFSOG
 * \ingroup mrpt_maps_grp
 */
class CBeacon : public mrpt::poses::CPointPDF
{
        DEFINE_SERIALIZABLE(CBeacon)
 
   public:
        /** The type for the IDs of landmarks.
         */
        using TBeaconID = int64_t;
 
        /** See m_typePDF
         */
        enum TTypePDF
        {
                pdfMonteCarlo = 0,
                pdfGauss,
                pdfSOG
        };
 
        /** Which one of the different 3D point PDF is currently used in this
         * object: montecarlo, gaussian, or a sum of gaussians.
         * \sa m_location
         */
        TTypePDF m_typePDF{pdfGauss};
 
        /** The individual PDF, if m_typePDF=pdfMonteCarlo (publicly accesible for
         * ease of use, but the CPointPDF interface is also implemented in CBeacon).
         */
        mrpt::poses::CPointPDFParticles m_locationMC{1};
        /** The individual PDF, if m_typePDF=pdfGauss (publicly accesible for ease
         * of use, but the CPointPDF interface is also implemented in CBeacon). */
        mrpt::poses::CPointPDFGaussian m_locationGauss;
        /** The individual PDF, if m_typePDF=pdfSOG (publicly accesible for ease of
         * use, but the CPointPDF interface is also implemented in CBeacon). */
        mrpt::poses::CPointPDFSOG m_locationSOG{1};
 
        /** An ID for the landmark (see details next...)
         *  This ID was introduced in the version 3 of this class (21/NOV/2006),
         *and its aim is
         *  to provide a way for easily establishing correspondences between
         *landmarks detected
         *  in sequential image frames. Thus, the management of this field should
         *be:
         *              - In 'servers' (classes/modules/... that detect landmarks from
         *images):
         *A different ID must be assigned to every landmark (e.g. a sequential
         *counter), BUT only in the case of being sure of the correspondence of one
         *landmark with another one in the past (e.g. tracking).
         *              - In 'clients': This field can be ignored, but if it is used, the
         *advantage is solving the correspondence between landmarks detected in
         *consequentive instants of time: Two landmarks with the same ID
         *<b>correspond</b> to the same physical feature, BUT it should not be
         *expected the inverse to be always true.
         *
         * Note that this field is never fill out automatically, it must be set by
         *the programmer if used.
         */
        TBeaconID m_ID{INVALID_BEACON_ID};
 
        /** Returns an estimate of the point, (the mean, or mathematical expectation
         * of the PDF).
         * \sa getCovariance
         */
        void getMean(mrpt::poses::CPoint3D& mean_point) const override;
 
        /** Returns an estimate of the point covariance matrix (3x3 cov matrix) and
         * the mean, both at once.
         * \sa getMean
         */
        void getCovarianceAndMean(
                mrpt::math::CMatrixDouble33& cov,
                mrpt::poses::CPoint3D& mean_point) const override;
 
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations) */
        void copyFrom(const mrpt::poses::CPointPDF& o) override;
 
        /** Save PDF's particles to a text file. See derived classes for more
         * information about the format of generated files */
        bool saveToTextFile(const std::string& file) const override;
 
        /** this = p (+) this. This can be used to convert a PDF from local
         * coordinates to global, providing the point (newReferenceBase) from which
         *   "to project" the current pdf. Result PDF substituted the currently
         * stored one in the object.
         */
        void changeCoordinatesReference(
                const mrpt::poses::CPose3D& newReferenceBase) override;
 
        /** Saves a 3D representation of the beacon into a given OpenGL scene  */
        void getAs3DObject(mrpt::opengl::CSetOfObjects::Ptr& outObj) const;
 
        /** Gets a set of MATLAB commands which draw the current state of the
         * beacon: */
        void getAsMatlabDrawCommands(std::vector<std::string>& out_Str) const;
 
        /** Draw a sample from the pdf. */
        void drawSingleSample(mrpt::poses::CPoint3D& outSample) const override;
 
        /** Bayesian fusion of two point distributions (product of two
         * distributions->new distribution), then save the result in this object
         * (WARNING: See implementing classes to see classes that can and cannot be
         * mixtured!)
         * \param p1 The first distribution to fuse
         * \param p2 The second distribution to fuse
         * \param minMahalanobisDistToDrop If set to different of 0, the result of
         * very separate Gaussian modes (that will result in negligible components)
         * in SOGs will be dropped to reduce the number of modes in the output.
         */
        void bayesianFusion(
                const CPointPDF& p1, const CPointPDF& p2,
                const double minMahalanobisDistToDrop = 0) override;
 
        /** Compute the observation model p(z_t|x_t) for a given observation (range
         * value), and return it as an approximate SOG.
         *  Note that if the beacon is a SOG itself, the number of gaussian modes
         * will be square.
         *  As a speed-up, if a "center point"+"maxDistanceFromCenter" is supplied
         * (maxDistanceFromCenter!=0), those modes farther than this sphere will be
         * discarded.
         *  Parameters such as the stdSigma of the sensor are gathered from
         * "myBeaconMap"
         *  The result is one "ring" for each Gaussian mode that represent the
         * beacon position in this object.
         *  The position of the sensor on the robot is used to shift the resulting
         * densities such as they represent the position of the robot, not the
         * sensor.
         *  \sa CBeaconMap::insertionOptions, generateRingSOG
         */
        void generateObservationModelDistribution(
                const float& sensedRange, mrpt::poses::CPointPDFSOG& outPDF,
                const CBeaconMap* myBeaconMap,
                const mrpt::poses::CPoint3D& sensorPntOnRobot,
                const mrpt::poses::CPoint3D& centerPoint =
                        mrpt::poses::CPoint3D(0, 0, 0),
                const float& maxDistanceFromCenter = 0) const;
 
        /** This static method returns a SOG with ring-shape (or as a 3D sphere)
         * that can be used to initialize a beacon if observed the first time.
         *  sensorPnt is the center of the ring/sphere, i.e. the absolute position
         * of the range sensor.
         *  If clearPreviousContentsOutPDF=false, the SOG modes will be added to
         * the current contents of outPDF
         *  If the 3x3 matrix covarianceCompositionToAdd is provided, it will be
         * add to every Gaussian (to model the composition of uncertainty).
         * \sa generateObservationModelDistribution
         */
        static void generateRingSOG(
                const float& sensedRange, mrpt::poses::CPointPDFSOG& outPDF,
                const CBeaconMap* myBeaconMap, const mrpt::poses::CPoint3D& sensorPnt,
                const mrpt::math::CMatrixDouble33* covarianceCompositionToAdd = nullptr,
                bool clearPreviousContentsOutPDF = true,
                const mrpt::poses::CPoint3D& centerPoint =
                        mrpt::poses::CPoint3D(0, 0, 0),
                const float& maxDistanceFromCenter = 0);
 
};  // End of class definition
 
}  // namespace maps
}  // namespace mrpt
 
#endif