CPosePDFGaussianInf.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 CPosePDFGaussianInf_H
#define CPosePDFGaussianInf_H

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

namespace mrpt
{
        namespace poses
        {

                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 );
                        typedef CPosePDFGaussianInf self_t;

                        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
                        mrpt::math::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 mrpt::math::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 MRPT_OVERRIDE {
                                mean_pose = mean;
                        }
                        bool isInfType() const MRPT_OVERRIDE { return true; }

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

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

                        /** 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 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;

                        /** 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 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 mrpt::math::CMatrixDouble33  &COV_01
                                        );

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

                        /** 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<mrpt::math::CVectorDouble> & outSamples ) const MRPT_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  CPosePDF &p1,const  CPosePDF &p2, const double &minMahalanobisDistToDrop = 0 ) MRPT_OVERRIDE;

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

                        /** 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.
                DEFINE_SERIALIZABLE_POST_CUSTOM_BASE( CPosePDFGaussianInf, CPosePDF );

                bool BASE_IMPEXP operator==(const CPosePDFGaussianInf &p1,const CPosePDFGaussianInf &p2);
                /** Pose compose operator: RES = A (+) B , computing both the mean and the covariance */
                CPosePDFGaussianInf BASE_IMPEXP operator +( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b  );
                /** Pose inverse compose operator: RES = A (-) B , computing both the mean and the covariance */
                CPosePDFGaussianInf BASE_IMPEXP operator -( const CPosePDFGaussianInf &a, const CPosePDFGaussianInf &b  );
                /** 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  );

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

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

#endif