CPosePDFGaussianInf.h

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/* +---------------------------------------------------------------------------+
   |          The Mobile Robot Programming Toolkit (MRPT) C++ library          |
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   |                       http://www.mrpt.org/                                |
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   |   Copyright (C) 2005-2011  University of Malaga                           |
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   |    This software was written by the Machine Perception and Intelligent    |
   |      Robotics Lab, University of Malaga (Spain).                          |
   |    Contact: Jose-Luis Blanco  <jlblanco@ctima.uma.es>                     |
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   |  This file is part of the MRPT project.                                   |
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   |     MRPT is free software: you can redistribute it and/or modify          |
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   +---------------------------------------------------------------------------+ */
#ifndef CPosePDFGaussianInf_H
#define CPosePDFGaussianInf_H

#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrixFixedNumeric.h>

namespace mrpt
{
namespace poses
{
        using namespace mrpt::math;

        class CPose3DPDF;

        // This must be added to any CSerializable derived class:
        DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPosePDFGaussianInf, CPosePDF )

        /** A Probability Density  function (PDF) of a 2D pose \f$ p(\mathbf{x}) = [x ~ y ~ \phi ]^t \f$ as a Gaussian with a mean and the inverse of the covariance.
         *
         *   This class implements a PDF as a mono-modal Gaussian distribution in its <b>information form</b>, that is,
         *     keeping the inverse of the covariance matrix instead of the covariance matrix itself.
         *
         *  This class is the dual of CPosePDFGaussian.
         *
         * \sa CPose2D, CPosePDF, CPosePDFParticles
         * \ingroup poses_pdf_grp
         */
        class BASE_IMPEXP CPosePDFGaussianInf : public CPosePDF
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CPosePDFGaussianInf )

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

         public:
                /** @name Data fields
                        @{ */

                CPose2D         mean;   //!< The mean value
                CMatrixDouble33         cov_inv;        //!< The inverse of the 3x3 covariance matrix (the "information" matrix)

                /** @} */

                inline const CPose2D & getPoseMean() const { return mean; }
                inline       CPose2D & getPoseMean()       { return mean; }

                /** Default constructor (mean=all zeros, inverse covariance=all zeros -> so be careful!) */
                CPosePDFGaussianInf();

                /** Constructor with a mean value (inverse covariance=all zeros -> so be careful!) */
                explicit CPosePDFGaussianInf( const CPose2D &init_Mean );

                /** Constructor */
                CPosePDFGaussianInf( const CPose2D &init_Mean, const CMatrixDouble33 &init_CovInv );

            /** Copy constructor, including transformations between other PDFs */
                explicit CPosePDFGaussianInf( const CPosePDF &o ) { copyFrom( o ); }

                /** Copy constructor, including transformations between other PDFs */
                explicit CPosePDFGaussianInf( const CPose3DPDF &o ) { copyFrom( o ); }


                 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF).
                   * \sa getCovariance
                   */
                void getMean(CPose2D &mean_pose) const {
                        mean_pose = mean;
                }

                /** Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.
                  * \sa getMean
                  */
                void getCovarianceAndMean(CMatrixDouble33 &cov,CPose2D &mean_point) const {
                        mean_point = mean;
                        this->cov_inv.inv(cov);
                }

                /** Returns the information (inverse covariance) matrix (a STATE_LEN x STATE_LEN matrix) \sa getMean, getCovarianceAndMean */
                virtual void getInformationMatrix(CMatrixDouble33 &inf) const { inf=cov_inv; }

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

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

                /** Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.
                 */
                void  saveToTextFile(const std::string &file) const;

                /** 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 );

                /** 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 CPose2D &newReferenceBase );

                /** Rotate the covariance matrix by replacing it by \f$ \mathbf{R}~\mathbf{COV}~\mathbf{R}^t \f$, where \f$ \mathbf{R} = \left[ \begin{array}{ccc} \cos\alpha & -\sin\alpha & 0 \\ \sin\alpha & \cos\alpha & 0 \\ 0 & 0 & 1 \end{array}\right] \f$.
                  */
                void  rotateCov(const double ang);

                /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (For 'x0' and 'x1' being independent variables!).
                  */
                void inverseComposition( const CPosePDFGaussianInf &x, const CPosePDFGaussianInf &ref  );

                /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (Given the 3x3 cross-covariance matrix of variables x0 and x1).
                  */
                void inverseComposition(
                        const CPosePDFGaussianInf &x1,
                        const CPosePDFGaussianInf &x0,
                        const CMatrixDouble33  &COV_01
                        );

                /** Draws a single sample from the distribution
                  */
                void  drawSingleSample( CPose2D &outPart ) const;

                /** Draws a number of samples from the distribution, and saves as a list of 1x3 vectors, where each row contains a (x,y,phi) datum.
                  */
                void  drawManySamples( size_t N, std::vector<vector_double> & outSamples ) const;

                /** 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  CPosePDF &p1,const  CPosePDF &p2, const double &minMahalanobisDistToDrop = 0 );

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

                /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated). */
                void  operator += ( const CPose2D &Ap);

                /** Evaluates the PDF at a given point.
                  */
                double  evaluatePDF( const CPose2D &x ) const;

                /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1].
                  */
                double  evaluateNormalizedPDF( const CPose2D &x ) const;

                /** Computes the Mahalanobis distance between the centers of two Gaussians.
                  */
                double  mahalanobisDistanceTo( const CPosePDFGaussianInf& theOther );

                /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated) (see formulas in jacobiansPoseComposition ).
                  */
                void  operator += ( const CPosePDFGaussianInf &Ap);

                /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated)
                  */
                inline void operator -=( const CPosePDFGaussianInf &ref  ) {
                        this->inverseComposition(*this,ref);
                }

        }; // End of class def.


        /** Pose compose operator: RES = A (+) B , computing both the mean and the covariance */
        inline CPosePDFGaussianInf operator +( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b  ) {
                CPosePDFGaussianInf res(a);
                res+=b;
                return res;
        }

        /** Pose inverse compose operator: RES = A (-) B , computing both the mean and the covariance */
        inline CPosePDFGaussianInf operator -( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b  ) {
                CPosePDFGaussianInf res;
                res.inverseComposition(a,b);
                return res;
        }

        /** Dumps the mean and covariance matrix to a text stream.
          */
        std::ostream BASE_IMPEXP & operator << (std::ostream & out, const CPosePDFGaussianInf& obj);

        /** Returns the Gaussian distribution of \f$ \mathbf{C} \f$, for \f$ \mathbf{C} = \mathbf{A} \oplus \mathbf{B} \f$.
          */
        poses::CPosePDFGaussianInf      BASE_IMPEXP operator + ( const mrpt::poses::CPose2D &A, const mrpt::poses::CPosePDFGaussianInf &B  );

        bool BASE_IMPEXP operator==(const CPosePDFGaussianInf &p1,const CPosePDFGaussianInf &p2);

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

#endif