CPose3DPDFGaussianInf.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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   |     along with MRPT.  If not, see <http://www.gnu.org/licenses/>.         |
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   +---------------------------------------------------------------------------+ */
#ifndef CPose3DPDFGaussianInf_H
#define CPose3DPDFGaussianInf_H

#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrixD.h>

namespace mrpt
{
namespace poses
{
        class CPosePDFGaussian;
        class CPose3DQuatPDFGaussian;

        DEFINE_SERIALIZABLE_PRE_CUSTOM_BASE( CPose3DPDFGaussianInf , CPose3DPDF )

        /** Declares a class that represents a Probability Density function (PDF) of a 3D pose \f$ p(\mathbf{x}) = [x ~ y ~ z ~ yaw ~ pitch ~ roll]^t \f$ as a Gaussian described by its mean and its inverse covariance matrix.
         *
         *   This class implements that PDF using a mono-modal Gaussian distribution in "information" form (inverse covariance matrix).
         *
         *  Uncertainty of pose composition operations (\f$ y = x \oplus u \f$) is implemented in the method "CPose3DPDFGaussianInf::operator+=".
         *
         *  For further details on implemented methods and the theory behind them,
         *  see <a href="http://www.mrpt.org/6D_poses:equivalences_compositions_and_uncertainty" >this report</a>.
         *
         * \sa CPose3D, CPose3DPDF, CPose3DPDFParticles, CPose3DPDFGaussian
         * \ingroup poses_pdf_grp
         */
        class BASE_IMPEXP CPose3DPDFGaussianInf : public CPose3DPDF
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CPose3DPDFGaussianInf )

        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
                        @{   */

                CPose3D                         mean;           //!< The mean value
                CMatrixDouble66         cov_inv;        //!< The inverse of the 6x6 covariance matrix

                /** @} */

                inline const CPose3D & getPoseMean() const { return mean; }
                inline       CPose3D & getPoseMean()       { return mean; }

                 /** Default constructor - mean: all zeros, inverse covariance=all zeros -> so be careful!
                  */
                CPose3DPDFGaussianInf();

                /** Constructor with a mean value, inverse covariance=all zeros -> so be careful! */
                explicit CPose3DPDFGaussianInf( const CPose3D &init_Mean );

                /** Uninitialized constructor: leave all fields uninitialized - Call with UNINITIALIZED_POSE as argument
                  */
                CPose3DPDFGaussianInf(TConstructorFlags_Poses constructor_dummy_param);

                /** Constructor with mean and inv cov. */
                CPose3DPDFGaussianInf( const CPose3D &init_Mean, const CMatrixDouble66 &init_CovInv );

                /** Constructor from a 6D pose PDF described as a Quaternion
                  */
                explicit CPose3DPDFGaussianInf( const CPose3DQuatPDFGaussian &o);

                 /** Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF).
                   * \sa getCovariance
                   */
                void getMean(CPose3D &mean_pose) const {
                        mean_pose = mean;
                }

                /** Returns an estimate of the pose covariance matrix (6x6 cov matrix) and the mean, both at once.
                  * \sa getMean
                  */
                void getCovarianceAndMean(CMatrixDouble66 &cov,CPose3D &mean_point) const {
                        mean_point = this->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(CMatrixDouble66 &inf) const { inf=cov_inv; }

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

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

                /** Copy from a 6D pose PDF described as a Quaternion
                  */
                void copyFrom( const CPose3DQuatPDFGaussian &o);

                /** Save the PDF to a text file, containing the 3D 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 );

                /** Draws a single sample from the distribution
                  */
                void  drawSingleSample( CPose3D &outPart ) const;

                /** Draws a number of samples from the distribution, and saves as a list of 1x6 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 CPose3DPDF &p1, const CPose3DPDF &p2 );

                /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF
                  */
                void     inverse(CPose3DPDF &o) const;

                /** Unary - operator, returns the PDF of the inverse pose.  */
                inline CPose3DPDFGaussianInf operator -() const
                {
                        CPose3DPDFGaussianInf p(UNINITIALIZED_POSE);
                        this->inverse(p);
                        return p;
                }

                /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated).
                  */
                void  operator += ( const CPose3D &Ap);

                /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated).
                  */
                void  operator += ( const CPose3DPDFGaussianInf &Ap);

                /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated).
                  */
                void  operator -= ( const CPose3DPDFGaussianInf &Ap);

                /** Evaluates the PDF at a given point.
                  */
                double  evaluatePDF( const CPose3D &x ) const;

                /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1].
                  */
                double  evaluateNormalizedPDF( const CPose3D &x ) const;

                /** Computes the Mahalanobis distance between the centers of two Gaussians.
                  *  The variables with a variance exactly equal to 0 are not taken into account in the process, but
                  *   "infinity" is returned if the corresponding elements are not exactly equal.
                  */
                double  mahalanobisDistanceTo( const CPose3DPDFGaussianInf& theOther);

                /** Returns a 3x3 matrix with submatrix of the inverse covariance for the variables (x,y,yaw) only.
                  */
                void getInvCovSubmatrix2D( CMatrixDouble &out_cov ) const;

        }; // End of class def.


        /** Pose composition for two 3D pose Gaussians  \sa CPose3DPDFGaussian::operator +=  */
        inline CPose3DPDFGaussianInf operator +( const CPose3DPDFGaussianInf &x, const CPose3DPDFGaussianInf &u )
        {
                CPose3DPDFGaussianInf   res(x);
                res+=u;
                return res;
        }

        /** Pose composition for two 3D pose Gaussians  \sa CPose3DPDFGaussianInf::operator -=  */
        inline CPose3DPDFGaussianInf operator -( const CPose3DPDFGaussianInf &x, const CPose3DPDFGaussianInf &u )
        {
                CPose3DPDFGaussianInf   res(x);
                res-=u;
                return res;
        }

        /** Dumps the mean and covariance matrix to a text stream.
          */
        std::ostream  BASE_IMPEXP & operator << (std::ostream & out, const CPose3DPDFGaussianInf& obj);

        bool BASE_IMPEXP operator==(const CPose3DPDFGaussianInf &p1,const CPose3DPDFGaussianInf &p2);

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

#endif