CPose3DPDFSOG.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 CPose3DPDFSOG_H
#define CPose3DPDFSOG_H

#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/utils/aligned_containers.h>

namespace mrpt
{
namespace poses
{
        // This must be added to any CSerializable derived class:
        DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPose3DPDFSOG, CPose3DPDF )

        /** Declares a class that represents a Probability Density function (PDF) of a 3D(6D) pose \f$ p(\mathbf{x}) = [x ~ y ~ z ~ yaw ~ pitch ~ roll]^t \f$.
         *   This class implements that PDF as the following multi-modal Gaussian distribution:
         *
         * \f$ p(\mathbf{x}) = \sum\limits_{i=1}^N \omega^i \mathcal{N}( \mathbf{x} ; \bar{\mathbf{x}}^i, \mathbf{\Sigma}^i )  \f$
         *
         *  Where the number of modes N is the size of CPose3DPDFSOG::m_modes. Angles are always in radians.
         *
         *  See mrpt::poses::CPose3DPDF for more details.
         * \ingroup poses_pdf_grp
         * \sa CPose3DPDF
         */
        class BASE_IMPEXP CPose3DPDFSOG : public CPose3DPDF
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CPose3DPDFSOG )

        public:
                /** The struct for each mode:
                 */
                struct BASE_IMPEXP TGaussianMode
                {
                        TGaussianMode() :
                                val(),
                                log_w(0)
                        { }

                        CPose3DPDFGaussian              val;

                        /** The log-weight
                          */
                        double          log_w;
                };

                typedef mrpt::aligned_containers<TGaussianMode>::vector_t  TModesList;
                typedef TModesList::const_iterator const_iterator;
                typedef TModesList::iterator iterator;

        protected:
                /** Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!)
                  */
                void  assureSymmetry();

                /** Access directly to this array for modify the modes as desired.
                  *  Note that no weight can be zero!!
                  *  We must use pointers to satisfy the mem-alignment of the matrixes
                  */
                TModesList   m_modes;

         public:
                /** Default constructor
                  * \param nModes The initial size of CPose3DPDFSOG::m_modes
                  */
                CPose3DPDFSOG( size_t nModes = 1 );

                void clear(); //!< Clear all the gaussian modes
                void resize(const size_t N); //!< Set the number of SOG modes
                size_t size() const { return m_modes.size(); } //!< Return the number of Gaussian modes.
                bool empty() const { return m_modes.empty(); } //!< Return whether there is any Gaussian mode.

                iterator begin() { return m_modes.begin(); }
                iterator end() { return m_modes.end(); }
                const_iterator begin() const { return m_modes.begin(); }
                const_iterator end()const { return m_modes.end(); }

                void getMean(CPose3D &mean_pose) const MRPT_OVERRIDE; //!< Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF), computed as a weighted average over all m_particles. \sa getCovariance
                void getCovarianceAndMean(mrpt::math::CMatrixDouble66 &cov,CPose3D &mean_point) const MRPT_OVERRIDE; //!< Returns an estimate of the pose covariance matrix (6x6 cov matrix) and the mean, both at once. \sa getMean
                void normalizeWeights(); //!< Normalize the weights in m_modes such as the maximum log-weight is 0.
                void getMostLikelyMode( CPose3DPDFGaussian& outVal ) const; //!< Return the Gaussian mode with the highest likelihood (or an empty Gaussian if there are no modes in this SOG)

                void copyFrom(const CPose3DPDF &o) MRPT_OVERRIDE; //!< Copy operator, translating if necesary (for example, between particles and gaussian representations)

                /** Save the density to a text file, with the following format:
                  *  There is one row per Gaussian "mode", and each row contains 10 elements:
                  *   - w (The linear weight)
                  *   - x_mean (gaussian mean value)
                  *   - y_mean (gaussian mean value)
                  *   - x_mean (gaussian mean value)
                  *   - yaw_mean (gaussian mean value, in radians)
                  *   - pitch_mean (gaussian mean value, in radians)
                  *   - roll_mean (gaussian mean value, in radians)
                  *   - C11,C22,C33,C44,C55,C66 (Covariance elements)
                  *   - C12,C13,C14,C15,C16 (Covariance elements)
                  *   - C23,C24,C25,C25 (Covariance elements)
                  *   - C34,C35,C36 (Covariance elements)
                  *   - C45,C46 (Covariance elements)
                  *   - C56 (Covariance elements)
                  *
                 */
                void saveToTextFile(const std::string &file) const MRPT_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. */
                void changeCoordinatesReference(const CPose3D &newReferenceBase ) MRPT_OVERRIDE;

                /** Bayesian fusion of two pose distributions, then save the result in this object (WARNING: Currently p1 must be a mrpt::poses::CPose3DPDFSOG object and p2 a mrpt::poses::CPose3DPDFSOG object) */
                void bayesianFusion( const CPose3DPDF &p1,const  CPose3DPDF &p2 ) MRPT_OVERRIDE;

                void drawSingleSample( CPose3D &outPart ) const MRPT_OVERRIDE; //!< Draws a single sample from the distribution
                void drawManySamples( size_t N, std::vector<mrpt::math::CVectorDouble> & outSamples ) const MRPT_OVERRIDE; //!< Draws a number of samples from the distribution, and saves as a list of 1x6 vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum
                void inverse(CPose3DPDF &o) const MRPT_OVERRIDE; //!< Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF

                void appendFrom( const CPose3DPDFSOG &o ); //!< Append the Gaussian modes from "o" to the current set of modes of "this" density

        }; // End of class def.
        DEFINE_SERIALIZABLE_POST_CUSTOM_BASE( CPose3DPDFSOG, CPose3DPDF )
        } // End of namespace
} // End of namespace
#endif