CPosePDFGaussian.h

Go to the documentation of this file.

/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2014, 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 CPosePDFGaussian_H
#define CPosePDFGaussian_H

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

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

        class CPose3DPDF;
        class CPoint2DPDFGaussian;

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

        /** Declares a class that represents a Probability Density  function (PDF) of a 2D pose \f$ p(\mathbf{x}) = [x ~ y ~ \phi ]^t \f$.
         *
         *   This class implements that PDF using a mono-modal Gaussian distribution. See mrpt::poses::CPosePDF for more details.
         *
         * \sa CPose2D, CPosePDF, CPosePDFParticles
         * \ingroup poses_pdf_grp
         */
        class BASE_IMPEXP CPosePDFGaussian : public CPosePDF
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CPosePDFGaussian )

        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;    //!< The 3x3 covariance matrix

                /** @} */

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

                /** Default constructor
                  */
                CPosePDFGaussian();

                /** Constructor
                  */
                explicit CPosePDFGaussian( const CPose2D &init_Mean );

                /** Constructor
                  */
                CPosePDFGaussian( const CPose2D &init_Mean, const CMatrixDouble33 &init_Cov );

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

                /** Copy constructor, including transformations between other PDFs */
                explicit CPosePDFGaussian( 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;
                        cov = this->cov;
                }

                /** 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 = 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 );

                /** 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 CPosePDFGaussian &x, const CPosePDFGaussian &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 CPosePDFGaussian &x1,
                        const CPosePDFGaussian &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 CPosePDFGaussian& theOther );

                /** Substitutes the diagonal elements if (square) they are below some given minimum values (Use this before bayesianFusion, for example, to avoid inversion of singular matrixes, etc...)  */
                void  assureMinCovariance( const double & minStdXY, const double &minStdPhi );

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

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

                /** Returns the PDF of the 2D point \f$ g = q \oplus l\f$ with "q"=this pose and "l" a point without uncertainty */
                void composePoint(const mrpt::math::TPoint2D &l, CPoint2DPDFGaussian &g ) const;


        }; // End of class def.


        /** Pose compose operator: RES = A (+) B , computing both the mean and the covariance */
        CPosePDFGaussian BASE_IMPEXP operator +( const CPosePDFGaussian &a, const CPosePDFGaussian &b  );

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

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

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

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

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

#endif