CPosePDF.h

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/* +------------------------------------------------------------------------+
   |                     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 CPOSEPDF_H
#define CPOSEPDF_H

#include <mrpt/utils/CSerializable.h>
#include <mrpt/poses/CPose2D.h>
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/utils/CProbabilityDensityFunction.h>

namespace mrpt
{
namespace poses
{
class CPosePDFGaussian;  // frd decl.

/** Declares a class that represents a probability density function (pdf) of a
 * 2D pose (x,y,phi).
 *   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.
 *
 *
 *  See also the tutorial on <a
 * href="http://www.mrpt.org/Probability_Density_Distributions_Over_Spatial_Representations"
 * >probabilistic spatial representations in the MRPT</a>.
 *
 * \sa CPose2D, CPose3DPDF, CPoseRandomSampler
 * \ingroup poses_pdf_grp
 */
class CPosePDF : public mrpt::utils::CSerializable,
                                 public mrpt::utils::CProbabilityDensityFunction<CPose2D, 3>
{
        DEFINE_VIRTUAL_SERIALIZABLE(CPosePDF)

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

        /** Bayesian fusion of two pose 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 CPosePDF& p1, const CPosePDF& p2,
                const double& minMahalanobisDistToDrop = 0) = 0;

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

        /** This static method computes the pose composition Jacobians, with these
           formulas:
                \code
                        df_dx =
                        [ 1, 0, -sin(phi_x)*x_u-cos(phi_x)*y_u ]
                        [ 0, 1,  cos(phi_x)*x_u-sin(phi_x)*y_u ]
                        [ 0, 0,                              1 ]

                        df_du =
                        [ cos(phi_x) , -sin(phi_x) ,  0  ]
                        [ sin(phi_x) ,  cos(phi_x) ,  0  ]
                        [         0  ,          0  ,  1  ]
                \endcode
          */
        static void jacobiansPoseComposition(
                const CPose2D& x, const CPose2D& u, mrpt::math::CMatrixDouble33& df_dx,
                mrpt::math::CMatrixDouble33& df_du, const bool compute_df_dx = true,
                const bool compute_df_du = true);

        /** \overload */
        static void jacobiansPoseComposition(
                const CPosePDFGaussian& x, const CPosePDFGaussian& u,
                mrpt::math::CMatrixDouble33& df_dx, mrpt::math::CMatrixDouble33& df_du);

        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; }
        /** Returns a 3D representation of this PDF (it doesn't clear the current
         * contents of out_obj, but append new OpenGL objects to that list)
          * \note Needs the mrpt-opengl library, and using
         * mrpt::opengl::CSetOfObjects::Ptr as template argument.
          * \note By default, ellipsoids for the confidence intervals of  "q=3" are
         * drawn; for more mathematical details, see
         * CGeneralizedEllipsoidTemplate::setQuantiles()
          */
        template <class OPENGL_SETOFOBJECTSPTR>
        inline void getAs3DObject(OPENGL_SETOFOBJECTSPTR& out_obj) const
        {
                typedef typename OPENGL_SETOFOBJECTSPTR::element_type SETOFOBJECTS;
                out_obj->insertCollection(*SETOFOBJECTS::posePDF2opengl(*this));
        }

        /** Returns a 3D representation of this PDF.
          * \note Needs the mrpt-opengl library, and using
         * mrpt::opengl::CSetOfObjects::Ptr as template argument.
          */
        template <class OPENGL_SETOFOBJECTSPTR>
        inline OPENGL_SETOFOBJECTSPTR getAs3DObject() const
        {
                typedef typename OPENGL_SETOFOBJECTSPTR::element_type SETOFOBJECTS;
                return SETOFOBJECTS::posePDF2opengl(*this);
        }

   public:
        MRPT_MAKE_ALIGNED_OPERATOR_NEW
};  // End of class def.

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

#endif