CPose3DPDFGaussianInf.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, 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 CPose3DPDFGaussianInf_H
#define CPose3DPDFGaussianInf_H

#include <mrpt/poses/CPose3D.h>
#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/math/CMatrixD.h>

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

/** 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 CPose3DPDFGaussianInf : public CPose3DPDF
{
    // This must be added to any CSerializable derived class:
    DEFINE_SERIALIZABLE(CPose3DPDFGaussianInf)
    using self_t = 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
        @{   */

    /** The mean value */
    CPose3D mean;
    /** The inverse of the 6x6 covariance matrix */
    mrpt::math::CMatrixDouble66 cov_inv;

    /** @} */

    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 mrpt::math::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 override { mean_pose = mean; }
    bool isInfType() const override { return true; }
    /** Returns an estimate of the pose covariance matrix (6x6 cov matrix) and
   * the mean, both at once.
   * \sa getMean  */
    void getCovarianceAndMean(
        mrpt::math::CMatrixDouble66& cov, CPose3D& mean_point) const override
    {
        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(
        mrpt::math::CMatrixDouble66& inf) const override
    {
        inf = cov_inv;
    }

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

    /** 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. */
    bool 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;

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

    /** 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<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 CPose3DPDF& p1, const CPose3DPDF& p2) override;

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

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

};  // End of class def.
bool operator==(
    const CPose3DPDFGaussianInf& p1, const CPose3DPDFGaussianInf& p2);
/** Pose composition for two 3D pose Gaussians  \sa CPose3DPDFGaussian::operator
 * +=  */
CPose3DPDFGaussianInf operator+(
    const CPose3DPDFGaussianInf& x, const CPose3DPDFGaussianInf& u);
/** Pose composition for two 3D pose Gaussians  \sa
 * CPose3DPDFGaussianInf::operator -=  */
CPose3DPDFGaussianInf operator-(
    const CPose3DPDFGaussianInf& x, const CPose3DPDFGaussianInf& u);
/** Dumps the mean and covariance matrix to a text stream. */
std::ostream& operator<<(std::ostream& out, const CPose3DPDFGaussianInf& obj);

}
#endif