CPose3DPDFGaussian.h

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/* +---------------------------------------------------------------------------+
   |                     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 CPose3DPDFGaussian_H
#define CPose3DPDFGaussian_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( CPose3DPDFGaussian , 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$.
         *
         *   This class implements that PDF using a mono-modal Gaussian distribution. See mrpt::poses::CPose3DPDF for more details.
         *
         *  Uncertainty of pose composition operations (\f$ y = x \oplus u \f$) is implemented in the method "CPose3DPDFGaussian::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
         * \ingroup poses_pdf_grp
         */
        class BASE_IMPEXP CPose3DPDFGaussian : public CPose3DPDF
        {
                // This must be added to any CSerializable derived class:
                DEFINE_SERIALIZABLE( CPose3DPDFGaussian )

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

         public:
                 /** Default constructor
                  */
                CPose3DPDFGaussian();

                /** Constructor
                  */
                explicit CPose3DPDFGaussian( const CPose3D &init_Mean );

                /** Uninitialized constructor: leave all fields uninitialized - Call with UNINITIALIZED_POSE as argument
                  */
                CPose3DPDFGaussian(TConstructorFlags_Poses constructor_dummy_param);

                /** Constructor  */
                CPose3DPDFGaussian( const CPose3D &init_Mean, const CMatrixDouble66 &init_Cov );

                /** Constructor from a Gaussian 2D pose PDF (sets to 0 the missing variables z,pitch, and roll).
                  */
                explicit CPose3DPDFGaussian( const CPosePDFGaussian &o );

                /** Constructor from a 6D pose PDF described as a Quaternion
                  */
                explicit CPose3DPDFGaussian( const CPose3DQuatPDFGaussian &o);

                /** The mean value
                 */
                CPose3D         mean;

                /** The 6x6 covariance matrix
                 */
                CMatrixDouble66         cov;

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

                 /** 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 {
                        cov = this->cov;
                        mean_point = this->mean;
                }

                /** 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 = 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 );

                /** 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 CPose3DPDFGaussian operator -() const
                {
                        CPose3DPDFGaussian 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 CPose3DPDFGaussian &Ap);

                /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated).
                  */
                void  operator -= ( const CPose3DPDFGaussian &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 CPose3DPDFGaussian& theOther);

                /** Returns a 3x3 matrix with submatrix of the covariance for the variables (x,y,yaw) only.
                  */
                void getCovSubmatrix2D( CMatrixDouble &out_cov ) const;


        }; // End of class def.


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

        /** Pose composition for two 3D pose Gaussians  \sa CPose3DPDFGaussian::operator -=  */
        inline CPose3DPDFGaussian operator -( const CPose3DPDFGaussian &x, const CPose3DPDFGaussian &u )
        {
                CPose3DPDFGaussian      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 CPose3DPDFGaussian& obj);

        bool BASE_IMPEXP operator==(const CPose3DPDFGaussian &p1,const CPose3DPDFGaussian &p2);

        } // End of namespace


        /** Global variables to change the run-time behaviour of some MRPT classes within mrpt-base.
          *  See each variable for the description of what classes it affects.
          */
        namespace global_settings
        {
                /** If set to true (false), a Scaled Unscented Transform is used instead of a linear approximation with Jacobians.
                  * Affects to:
                  *             - CPose3DPDFGaussian::CPose3DPDFGaussian( const CPose3DQuatPDFGaussian &o)
                  */
                extern BASE_IMPEXP bool USE_SUT_QUAT2EULER_CONVERSION;
        }

} // End of namespace

#endif