CPointPDFParticles.h

Go to the documentation of this file.

/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */
#pragma once

#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/bayes/CParticleFilterData.h>
#include <mrpt/math/CMatrixF.h>
#include <mrpt/math/TPoint3D.h>
#include <mrpt/poses/CPointPDF.h>

namespace mrpt::poses
{
/** A probability distribution of a 2D/3D point, represented as a set of random
 * samples (particles).
 * \sa CPointPDF
 * \ingroup poses_pdf_grp
 */
class CPointPDFParticles
    : public CPointPDF,
      public mrpt::bayes::CParticleFilterData<mrpt::math::TPoint3Df>,
      public mrpt::bayes::CParticleFilterDataImpl<
          CPointPDFParticles, mrpt::bayes::CParticleFilterData<
                                  mrpt::math::TPoint3Df>::CParticleList>
{
    DEFINE_SERIALIZABLE(CPointPDFParticles)

   public:
    /** Default constructor */
    CPointPDFParticles(size_t numParticles = 1);

    /** Clear all the particles (free memory) */
    void clear();

    /** Erase all the previous particles and change the number of particles,
     * with a given initial value  */
    void setSize(
        size_t numberParticles,
        const mrpt::math::TPoint3Df& defaultValue = mrpt::math::TPoint3Df{0, 0,
                                                                          0});

    /** Returns the number of particles */
    size_t size() const { return m_particles.size(); }

    void getMean(CPoint3D& mean_point) const override;

    std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override;

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

    /** Save PDF's particles to a text file, where each line is: X Y Z LOG_W */
    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. Both the mean value and the covariance matrix
     * are updated correctly.  */
    void changeCoordinatesReference(const CPose3D& newReferenceBase) override;

    /** Compute the kurtosis of the distribution */
    double computeKurtosis();

    /** Draw a sample from the pdf */
    void drawSingleSample(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;

};  // End of class def.
}  // namespace mrpt::poses