CPoint2DPDFGaussian.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 CPoint2DPDFGaussian_H
#define CPoint2DPDFGaussian_H
 
#include <mrpt/poses/CPoint2DPDF.h>
 
namespace mrpt
{
namespace poses
{
/** A gaussian distribution for 2D points. Also a method for bayesian fusion is
 * provided.
 * \ingroup poses_pdf_grp
 * \sa CPoint2DPDF
 */
class CPoint2DPDFGaussian : public CPoint2DPDF
{
        DEFINE_SERIALIZABLE(CPoint2DPDFGaussian)
 
   public:
        /** Default constructor */
        CPoint2DPDFGaussian();
        /** Constructor */
        CPoint2DPDFGaussian(const CPoint2D& init_Mean);
        /** Constructor */
        CPoint2DPDFGaussian(
                const CPoint2D& init_Mean, const mrpt::math::CMatrixDouble22& init_Cov);
 
        /** The mean value */
        CPoint2D mean;
        /** The 2x2 covariance matrix */
        mrpt::math::CMatrixDouble22 cov;
 
        /** Returns an estimate of the point, (the mean, or mathematical expectation
         * of the PDF) */
        void getMean(CPoint2D& p) const override { p = this->mean; }
        /** Returns an estimate of the point covariance matrix (2x2 cov matrix) and
         * the mean, both at once. \sa getMean */
        void getCovarianceAndMean(
                mrpt::math::CMatrixDouble22& out_cov,
                CPoint2D& mean_point) const override
        {
                out_cov = this->cov;
                mean_point = this->mean;
        }
 
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations) */
        void copyFrom(const CPoint2DPDF& o) override;
 
        /** Save PDF's particles to a text file, containing the 2D pose in the first
         * line, then the covariance matrix in next 3 lines */
        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;
 
        /** Bayesian fusion of two points gauss. distributions, then save the result
          *in this object.
          *  The process is as follows:<br>
          *             - (x1,S1): Mean and variance of the p1 distribution.
          *             - (x2,S2): Mean and variance of the p2 distribution.
          *             - (x,S): Mean and variance of the resulting distribution.
          *
          *    S = (S1<sup>-1</sup> + S2<sup>-1</sup>)<sup>-1</sup>;
          *    x = S * ( S1<sup>-1</sup>*x1 + S2<sup>-1</sup>*x2 );
          */
        void bayesianFusion(
                const CPoint2DPDFGaussian& p1, const CPoint2DPDFGaussian& p2);
 
        /** Computes the "correspondence likelihood" of this PDF with another one:
         * This is implemented as the integral from -inf to +inf of the product of
         * both PDF.
          * The resulting number is >=0.
          * \sa productIntegralNormalizedWith
          * \exception std::exception On errors like covariance matrix with null
         * determinant, etc...
          */
        double productIntegralWith(const CPoint2DPDFGaussian& p) const;
 
        /** Computes the "correspondence likelihood" of this PDF with another one:
         * This is implemented as the integral from -inf to +inf of the product of
         * both PDF.
          * The resulting number is in the range [0,1].
          *  Note that the resulting value is in fact
          *  \f[ exp( -\frac{1}{2} D^2 ) \f]
          *  , with \f$ D^2 \f$ being the square Mahalanobis distance between the
         * two pdfs.
          * \sa productIntegralWith
          * \exception std::exception On errors like covariance matrix with null
         * determinant, etc...
          */
        double productIntegralNormalizedWith(const CPoint2DPDFGaussian& p) const;
 
        /** Draw a sample from the pdf */
        void drawSingleSample(CPoint2D& 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 CPoint2DPDF& p1, const CPoint2DPDF& p2,
                const double minMahalanobisDistToDrop = 0) override;
 
        /** Returns the Mahalanobis distance from this PDF to another PDF, that is,
         * it's evaluation at (0,0,0) */
        double mahalanobisDistanceTo(const CPoint2DPDFGaussian& other) const;
        /** Returns the Mahalanobis distance from this PDF to some point */
        double mahalanobisDistanceToPoint(const double x, const double y) const;
 
};  // End of class def.
}  // End of namespace
}  // End of namespace
#endif