CPosePDFGaussianInf.h

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/* +------------------------------------------------------------------------+
   |                     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:

/** 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 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
                @{ */

        /** The mean value */
        CPose2D mean;
        /** The inverse of the 3x3 covariance matrix (the "information" matrix) */
        mrpt::math::CMatrixDouble33 cov_inv;

        /** @} */

        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 override { mean_pose = mean; }
        bool isInfType() const 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 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 override
        {
                inf = cov_inv;
        }

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

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

bool operator==(const CPosePDFGaussianInf& p1, const CPosePDFGaussianInf& p2);
/** Pose compose operator: RES = A (+) B , computing both the mean and the
 * covariance */
CPosePDFGaussianInf operator+(
        const CPosePDFGaussianInf& a, const CPosePDFGaussianInf& b);
/** Pose inverse compose operator: RES = A (-) B , computing both the mean and
 * the covariance */
CPosePDFGaussianInf 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 operator+(
        const mrpt::poses::CPose2D& A, const mrpt::poses::CPosePDFGaussianInf& B);

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

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

#endif