CPosePDFParticles.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 CPosePDFParticles_H
#define CPosePDFParticles_H

#include <mrpt/poses/CPosePDF.h>
#include <mrpt/poses/CPoseRandomSampler.h>
#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/bayes/CParticleFilterData.h>
#include <mrpt/math/lightweight_geom_data.h>

namespace mrpt::poses
{
/** Declares a class that represents a Probability Density Function (PDF) over a
 * 2D pose (x,y,phi), using a set of weighted samples.
 *
 *  This class is also the base for the implementation of Monte-Carlo
 * Localization (MCL), in mrpt::slam::CMonteCarloLocalization2D.
 *
 *  See the application "app/pf-localization" for an example of usage.
 *
 * \sa CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable
 * \ingroup poses_pdf_grp
 */
class CPosePDFParticles
    : public CPosePDF,
      public mrpt::bayes::CParticleFilterData<
          mrpt::math::TPose2D, mrpt::bayes::particle_storage_mode::VALUE>,
      public mrpt::bayes::CParticleFilterDataImpl<
          CPosePDFParticles,
          mrpt::bayes::CParticleFilterData<
              mrpt::math::TPose2D,
              mrpt::bayes::particle_storage_mode::VALUE>::CParticleList>
{
    DEFINE_SERIALIZABLE(CPosePDFParticles)

   public:
    /** Free all the memory associated to m_particles, and set the number of
     * parts = 0 */
    void clear();

    /** Constructor
     * \param M The number of m_particles.
     */
    CPosePDFParticles(size_t M = 1);

    /** Copy operator, translating if necesary (for example, between m_particles
     * and gaussian representations)
     */
    void copyFrom(const CPosePDF& o) override;

    /** Reset the PDF to a single point: All m_particles will be set exactly to
     * the supplied pose.
     * \param location The location to set all the m_particles.
     * \param particlesCount If this is set to 0 the number of m_particles
     * remains unchanged.
     *  \sa resetUniform, resetUniformFreeSpace, resetAroundSetOfPoses
     */
    void resetDeterministic(
        const mrpt::math::TPose2D& location, size_t particlesCount = 0);

    /** Reset the PDF to an uniformly distributed one, inside of the defined
     * cube.
     * If particlesCount is set to -1 the number of m_particles remains
     * unchanged.
     *  \sa resetDeterministic, resetUniformFreeSpace, resetAroundSetOfPoses
     */
    void resetUniform(
        const double x_min, const double x_max, const double y_min,
        const double y_max, const double phi_min = -M_PI,
        const double phi_max = M_PI, const int particlesCount = -1);

    /** Reset the PDF to a multimodal distribution over a set of "spots"
     * (x,y,phi)
     * The total number of particles will be `list_poses.size() *
     * num_particles_per_pose`.
     * \param[in] list_poses The poses (x,y,phi) around which particles will be
     * spread. Must contains at least one pose.
     * \param[in] num_particles_per_pose Number of particles to be spread
     * around each of the "spots" in list_poses. Must be >=1.
     *
     * Particles will be spread uniformly in a box of width
     * `spread_{x,y,phi_rad}` in each of
     * the three coordinates (meters, radians), so it can be understood as the
     * "initial uncertainty".
     *
     *  \sa resetDeterministic, resetUniformFreeSpace
     */
    void resetAroundSetOfPoses(
        const std::vector<mrpt::math::TPose2D>& list_poses,
        const size_t num_particles_per_pose, const double spread_x,
        const double spread_y, const double spread_phi_rad);

    /** Returns an estimate of the pose, (the mean, or mathematical expectation
     * of the PDF).
     * \sa getCovariance
     */
    void getMean(CPose2D& mean_pose) const override;

    /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and
     * the mean, both at once.
     * \sa getMean
     */
    void getCovarianceAndMean(
        mrpt::math::CMatrixDouble33& cov, CPose2D& mean_point) const override;

    /** Returns the pose of the i'th particle.
     */
    mrpt::math::TPose2D getParticlePose(size_t i) const;

    /** Save PDF's m_particles to a text file. In each line it will go: "x y phi
     * weight"
     */
    bool saveToTextFile(const std::string& file) const override;

    /** Get the m_particles count (equivalent to "particlesCount")
     */
    inline size_t size() const { return m_particles.size(); }
    /** 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 CPose3D& newReferenceBase) override;

    /** Draws a single sample from the distribution (WARNING: weights are
     * assumed to be normalized!)
     */
    void drawSingleSample(CPose2D& outPart) const override;

    /** Appends (pose-composition) a given pose "p" to each particle
     */
    void operator+=(const mrpt::math::TPose2D& Ap);

    /** Appends (add to the list) a set of m_particles to the existing ones, and
     * then normalize weights.
     */
    void append(CPosePDFParticles& o);

    /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF
     */
    void inverse(CPosePDF& o) const override;

    /** Returns the particle with the highest weight.
     */
    mrpt::math::TPose2D getMostLikelyParticle() const;

    /** Bayesian fusion.
     */
    void bayesianFusion(
        const CPosePDF& p1, const CPosePDF& p2,
        const double minMahalanobisDistToDrop = 0) override;

    /** Evaluates the PDF at a given arbitrary point as reconstructed by a
     * Parzen window.
     * \sa saveParzenPDFToTextFile
     */
    double evaluatePDF_parzen(
        const double x, const double y, const double phi,
        const double stdXY, const double stdPhi) const;

    /** Save a text file (compatible with matlab) representing the 2D evaluation
     * of the PDF as reconstructed by a Parzen window.
     * \sa evaluatePDF_parzen
     */
    void saveParzenPDFToTextFile(
        const char* fileName, const double x_min, const double x_max,
        const double y_min, const double y_max, const double phi,
        const double stepSizeXY, const double stdXY, const double stdPhi) const;

};  // End of class def.

}
#endif