CPoint2DPDF.h

Go to the documentation of this file.

/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2017, 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 CPoint2DPDF_H
#define CPoint2DPDF_H

#include <mrpt/utils/CSerializable.h>
#include <mrpt/utils/CProbabilityDensityFunction.h>
#include <mrpt/poses/CPoint2D.h>

namespace mrpt
{
namespace poses
{
/** Declares a class that represents a Probability Distribution function (PDF)
 * of a 2D point (x,y).
 *   This class is just the base class for unifying many diferent
 *    ways this PDF can be implemented.
 *
 *  For convenience, a pose composition is also defined for any
 *    PDF derived class, changeCoordinatesReference, in the form of a method
 * rather than an operator.
 *
 *  For a similar class for 6D poses (a 3D point with attitude), see CPose3DPDF
 *
 *  See also the tutorial on <a
 * href="http://www.mrpt.org/Probability_Density_Distributions_Over_Spatial_Representations"
 * >probabilistic spatial representations in the MRPT</a>.
 * \ingroup poses_pdf_grp
 * \sa CPoint2D, CPointPDF
 */
class CPoint2DPDF : public mrpt::utils::CSerializable,
                                        public mrpt::utils::CProbabilityDensityFunction<CPoint2D, 2>
{
        DEFINE_VIRTUAL_SERIALIZABLE(CPoint2DPDF)

   public:
        /** Copy operator, translating if necesary (for example, between particles
         * and gaussian representations)
          */
        virtual void copyFrom(const CPoint2DPDF& o) = 0;

        /** 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.
          */
        virtual void bayesianFusion(
                const CPoint2DPDF& p1, const CPoint2DPDF& p2,
                const double& minMahalanobisDistToDrop = 0) = 0;

        enum
        {
                is_3D_val = 0
        };
        static inline bool is_3D() { return is_3D_val != 0; }
        enum
        {
                is_PDF_val = 1
        };
        static inline bool is_PDF() { return is_PDF_val != 0; }
};  // End of class def.

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

#endif