CPosePDFGaussianInf.cpp

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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 |
   +------------------------------------------------------------------------+ */
 
#include "poses-precomp.h"  // Precompiled headers
 
#include <mrpt/poses/CPosePDFGaussianInf.h>
#include <mrpt/poses/CPosePDFGaussian.h>
#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPose3DPDFGaussianInf.h>
#include <mrpt/poses/CPose3D.h>
#include <mrpt/math/distributions.h>
#include <mrpt/math/wrap2pi.h>
#include <mrpt/system/os.h>
#include <mrpt/serialization/CArchive.h>
#include <mrpt/random.h>
 
using namespace mrpt;
using namespace mrpt::poses;
using namespace mrpt::math;
using namespace mrpt::random;
using namespace mrpt::system;
 
using namespace std;
 
IMPLEMENTS_SERIALIZABLE(CPosePDFGaussianInf, CPosePDF, mrpt::poses)
 
/*---------------------------------------------------------------
        Constructor
  ---------------------------------------------------------------*/
CPosePDFGaussianInf::CPosePDFGaussianInf() : mean(0, 0, 0), cov_inv() {}
/*---------------------------------------------------------------
        Constructor
  ---------------------------------------------------------------*/
CPosePDFGaussianInf::CPosePDFGaussianInf(
        const CPose2D& init_Mean, const CMatrixDouble33& init_CovInv)
        : mean(init_Mean), cov_inv(init_CovInv)
{
}
 
/*---------------------------------------------------------------
        Constructor
  ---------------------------------------------------------------*/
CPosePDFGaussianInf::CPosePDFGaussianInf(const CPose2D& init_Mean)
        : mean(init_Mean), cov_inv()
{
}
 
uint8_t CPosePDFGaussianInf::serializeGetVersion() const { return 0; }
void CPosePDFGaussianInf::serializeTo(mrpt::serialization::CArchive& out) const
{
        out << mean.x() << mean.y() << mean.phi();
        out << cov_inv(0, 0) << cov_inv(1, 1) << cov_inv(2, 2);
        out << cov_inv(0, 1) << cov_inv(0, 2) << cov_inv(1, 2);
}
void CPosePDFGaussianInf::serializeFrom(
        mrpt::serialization::CArchive& in, uint8_t version)
{
        switch (version)
        {
                case 0:
                {
                        TPose2D p;
                        in >> p.x >> p.y >> p.phi;
                        mean = CPose2D(p);
 
                        in >> cov_inv(0, 0) >> cov_inv(1, 1) >> cov_inv(2, 2);
                        in >> cov_inv(0, 1) >> cov_inv(0, 2) >> cov_inv(1, 2);
                }
                break;
                default:
                        MRPT_THROW_UNKNOWN_SERIALIZATION_VERSION(version)
        };
}
 
/*---------------------------------------------------------------
                                                copyFrom
  ---------------------------------------------------------------*/
void CPosePDFGaussianInf::copyFrom(const CPosePDF& o)
{
        if (this == &o) return;  // It may be used sometimes
 
        if (IS_CLASS(&o, CPosePDFGaussianInf))
        {  // It's my same class:
                const CPosePDFGaussianInf* ptr =
                        static_cast<const CPosePDFGaussianInf*>(&o);
                mean = ptr->mean;
                cov_inv = ptr->cov_inv;
        }
        else
        {  // Convert to gaussian pdf:
                o.getMean(mean);
 
                CMatrixDouble33 o_cov(UNINITIALIZED_MATRIX);
                o.getCovariance(o_cov);
                o_cov.inv_fast(this->cov_inv);
        }
}
 
/*---------------------------------------------------------------
                                                copyFrom 3D
  ---------------------------------------------------------------*/
void CPosePDFGaussianInf::copyFrom(const CPose3DPDF& o)
{
        // Convert to gaussian pdf:
        mean = CPose2D(o.getMeanVal());
 
        if (IS_CLASS(&o, CPose3DPDFGaussianInf))
        {  // Cov is already in information form:
                const CPose3DPDFGaussianInf* ptr =
                        static_cast<const CPose3DPDFGaussianInf*>(&o);
                cov_inv(0, 0) = ptr->cov_inv(0, 0);
                cov_inv(1, 1) = ptr->cov_inv(1, 1);
                cov_inv(2, 2) = ptr->cov_inv(3, 3);
                cov_inv(0, 1) = cov_inv(1, 0) = ptr->cov_inv(0, 1);
                cov_inv(0, 2) = cov_inv(2, 0) = ptr->cov_inv(0, 3);
                cov_inv(1, 2) = cov_inv(2, 1) = ptr->cov_inv(1, 3);
        }
        else
        {
                CMatrixDouble66 C(UNINITIALIZED_MATRIX);
                o.getCovariance(C);
 
                // Clip to 3x3:
                CMatrixDouble33 o_cov(UNINITIALIZED_MATRIX);
                o_cov(0, 0) = C(0, 0);
                o_cov(1, 1) = C(1, 1);
                o_cov(2, 2) = C(3, 3);
                o_cov(0, 1) = o_cov(1, 0) = C(0, 1);
                o_cov(0, 2) = o_cov(2, 0) = C(0, 3);
                o_cov(1, 2) = o_cov(2, 1) = C(1, 3);
 
                o_cov.inv_fast(this->cov_inv);
        }
}
 
/*---------------------------------------------------------------
 
  ---------------------------------------------------------------*/
bool CPosePDFGaussianInf::saveToTextFile(const std::string& file) const
{
        FILE* f = os::fopen(file.c_str(), "wt");
        if (!f) return false;
 
        os::fprintf(f, "%f %f %f\n", mean.x(), mean.y(), mean.phi());
 
        for (unsigned int i = 0; i < 3; i++)
                os::fprintf(
                        f, "%f %f %f\n", cov_inv(i, 0), cov_inv(i, 1), cov_inv(i, 2));
 
        os::fclose(f);
        return true;
}
 
/*---------------------------------------------------------------
                                                changeCoordinatesReference
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::changeCoordinatesReference(
        const CPose3D& newReferenceBase_)
{
        const CPose2D newReferenceBase = CPose2D(newReferenceBase_);
 
        // The mean:
        mean.composeFrom(newReferenceBase, mean);
 
        // The covariance:
        rotateCov(newReferenceBase.phi());
}
 
/*---------------------------------------------------------------
                                                changeCoordinatesReference
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::changeCoordinatesReference(
        const CPose2D& newReferenceBase)
{
        // The mean:
        mean.composeFrom(newReferenceBase, mean);
        // The covariance:
        rotateCov(newReferenceBase.phi());
}
 
/*---------------------------------------------------------------
                                                changeCoordinatesReference
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::rotateCov(const double ang)
{
        const double ccos = cos(ang);
        const double ssin = sin(ang);
 
        alignas(MRPT_MAX_ALIGN_BYTES)
                const double rot_vals[] = {ccos, -ssin, 0., ssin, ccos, 0., 0., 0., 1.};
 
        const CMatrixFixedNumeric<double, 3, 3> rot(rot_vals);
 
        // NEW_COV = H C H^T
        // NEW_COV^(-1) = (H C H^T)^(-1) = (H^T)^(-1) C^(-1) H^(-1)
        // rot: Inverse of a rotation matrix is its trasposed.
        //      But we need H^t^-1 -> H !! so rot stays unchanged:
        cov_inv = (rot * cov_inv * rot.adjoint()).eval();
}
 
/*---------------------------------------------------------------
                                        drawSingleSample
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::drawSingleSample(CPose2D& outPart) const
{
        MRPT_START
 
        CMatrixDouble33 cov(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(cov);
 
        CVectorDouble v;
        getRandomGenerator().drawGaussianMultivariate(v, cov);
 
        outPart.x(mean.x() + v[0]);
        outPart.y(mean.y() + v[1]);
        outPart.phi(mean.phi() + v[2]);
 
        // Range -pi,pi
        outPart.normalizePhi();
 
        MRPT_END_WITH_CLEAN_UP(
                cov_inv.saveToTextFile(
                        "__DEBUG_EXC_DUMP_drawSingleSample_COV_INV.txt"););
}
 
/*---------------------------------------------------------------
                                        drawManySamples
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::drawManySamples(
        size_t N, std::vector<CVectorDouble>& outSamples) const
{
        MRPT_START
 
        CMatrixDouble33 cov(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(cov);
 
        std::vector<CVectorDouble> rndSamples;
 
        getRandomGenerator().drawGaussianMultivariateMany(rndSamples, N, cov);
        outSamples.resize(N);
        for (unsigned int i = 0; i < N; i++)
        {
                outSamples[i].resize(3);
                outSamples[i][0] = mean.x() + rndSamples[i][0];
                outSamples[i][1] = mean.y() + rndSamples[i][1];
                outSamples[i][2] = mean.phi() + rndSamples[i][2];
 
                wrapToPiInPlace(outSamples[i][2]);
        }
 
        MRPT_END
}
 
/*---------------------------------------------------------------
                                        bayesianFusion
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::bayesianFusion(
        const CPosePDF& p1_, const CPosePDF& p2_,
        const double minMahalanobisDistToDrop)
{
        MRPT_START
 
        MRPT_UNUSED_PARAM(minMahalanobisDistToDrop);  // Not used in this class!
 
        ASSERT_(p1_.GetRuntimeClass() == CLASS_ID(CPosePDFGaussianInf));
        ASSERT_(p2_.GetRuntimeClass() == CLASS_ID(CPosePDFGaussianInf));
 
        const CPosePDFGaussianInf* p1 =
                static_cast<const CPosePDFGaussianInf*>(&p1_);
        const CPosePDFGaussianInf* p2 =
                static_cast<const CPosePDFGaussianInf*>(&p2_);
 
        const CMatrixDouble33& C1_inv = p1->cov_inv;
        const CMatrixDouble33& C2_inv = p2->cov_inv;
 
        CMatrixDouble31 x1 = CMatrixDouble31(p1->mean);
        CMatrixDouble31 x2 = CMatrixDouble31(p2->mean);
 
        this->cov_inv = C1_inv + C2_inv;
 
        CMatrixDouble33 cov(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(cov);
 
        CMatrixDouble31 x = cov * (C1_inv * x1 + C2_inv * x2);
 
        this->mean.x(x(0, 0));
        this->mean.y(x(1, 0));
        this->mean.phi(x(2, 0));
        this->mean.normalizePhi();
 
        MRPT_END
}
 
/*---------------------------------------------------------------
                                        inverse
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::inverse(CPosePDF& o) const
{
        ASSERT_(o.GetRuntimeClass() == CLASS_ID(CPosePDFGaussianInf));
        CPosePDFGaussianInf* out = static_cast<CPosePDFGaussianInf*>(&o);
 
        // The mean:
        out->mean = CPose2D(0, 0, 0) - mean;
 
        // The covariance:
        const double ccos = ::cos(mean.phi());
        const double ssin = ::sin(mean.phi());
 
        // jacobian:
        alignas(MRPT_MAX_ALIGN_BYTES) const double H_values[] = {
                -ccos, -ssin, mean.x() * ssin - mean.y() * ccos,
                ssin,  -ccos, mean.x() * ccos + mean.y() * ssin,
                0,       0,      -1};
        const CMatrixFixedNumeric<double, 3, 3> H(H_values);
 
        out->cov_inv.noalias() =
                (H * cov_inv * H.adjoint()).eval();  // o.cov = H * cov * Ht. It's the
        // same with inverse covariances.
}
 
/*---------------------------------------------------------------
                                                        +=
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::operator+=(const CPose2D& Ap)
{
        mean = mean + Ap;
        rotateCov(Ap.phi());
}
 
/*---------------------------------------------------------------
                                                evaluatePDF
 ---------------------------------------------------------------*/
double CPosePDFGaussianInf::evaluatePDF(const CPose2D& x) const
{
        CMatrixDouble31 X = CMatrixDouble31(x);
        CMatrixDouble31 MU = CMatrixDouble31(mean);
 
        return math::normalPDF(X, MU, cov_inv.inverse());
}
 
/*---------------------------------------------------------------
                                                evaluateNormalizedPDF
 ---------------------------------------------------------------*/
double CPosePDFGaussianInf::evaluateNormalizedPDF(const CPose2D& x) const
{
        CMatrixDouble31 X = CMatrixDouble31(x);
        CMatrixDouble31 MU = CMatrixDouble31(mean);
 
        CMatrixDouble33 cov(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(cov);
 
        return math::normalPDF(X, MU, cov) / math::normalPDF(MU, MU, cov);
}
 
/*---------------------------------------------------------------
                                                assureSymmetry
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::assureSymmetry()
{
        // Differences, when they exist, appear in the ~15'th significant
        //  digit, so... just take one of them arbitrarily!
        cov_inv(0, 1) = cov_inv(1, 0);
        cov_inv(0, 2) = cov_inv(2, 0);
        cov_inv(1, 2) = cov_inv(2, 1);
}
 
/*---------------------------------------------------------------
                                                mahalanobisDistanceTo
 ---------------------------------------------------------------*/
double CPosePDFGaussianInf::mahalanobisDistanceTo(
        const CPosePDFGaussianInf& theOther)
{
        MRPT_START
 
        CArrayDouble<3> MU = CArrayDouble<3>(mean);
        MU -= CArrayDouble<3>(theOther.mean);
 
        wrapToPiInPlace(MU[2]);
 
        if (MU[0] == 0 && MU[1] == 0 && MU[2] == 0)
                return 0;  // This is the ONLY case where we know the result, whatever
        // COVs are.
 
        CMatrixDouble33 COV_(UNINITIALIZED_MATRIX), cov2(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(COV_);
        theOther.cov_inv.inv(cov2);
 
        COV_ += cov2;  // COV_ = cov1+cov2
 
        CMatrixDouble33 COV_inv(UNINITIALIZED_MATRIX);
        COV_.inv_fast(COV_inv);
 
        // (~MU) * (!COV_) * MU
        return std::sqrt(mrpt::math::multiply_HCHt_scalar(MU, COV_inv));
 
        MRPT_END
}
 
/*---------------------------------------------------------------
                                                operator <<
 ---------------------------------------------------------------*/
std::ostream& mrpt::poses::operator<<(
        std::ostream& out, const CPosePDFGaussianInf& obj)
{
        out << "Mean: " << obj.mean << "\n";
        out << "Inverse cov:\n" << obj.cov_inv << "\n";
 
        return out;
}
 
/*---------------------------------------------------------------
                                                operator +
 ---------------------------------------------------------------*/
poses::CPosePDFGaussianInf operator+(
        const mrpt::poses::CPose2D& A, const mrpt::poses::CPosePDFGaussianInf& B)
{
        poses::CPosePDFGaussianInf ret(B);
        ret.changeCoordinatesReference(A);
        return ret;
}
 
/*---------------------------------------------------------------
                                                inverseComposition
  Set 'this' = 'x' - 'ref', computing the mean using the "-"
        operator and the covariances through the corresponding Jacobians.
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::inverseComposition(
        const CPosePDFGaussianInf& xv, const CPosePDFGaussianInf& xi)
{
        // Use implementation in CPosePDFGaussian:
        CMatrixDouble33 xv_cov(UNINITIALIZED_MATRIX), xi_cov(UNINITIALIZED_MATRIX);
        xv.cov_inv.inv(xv_cov);
        xi.cov_inv.inv(xi_cov);
 
        const CPosePDFGaussian xv_(xv.mean, xv_cov);
        const CPosePDFGaussian xi_(xi.mean, xi_cov);
 
        CPosePDFGaussian RET;
        RET.inverseComposition(xv_, xi_);
 
        // Copy result to "this":
        this->mean = RET.mean;
        RET.cov.inv(this->cov_inv);
}
 
/*---------------------------------------------------------------
                                                inverseComposition
  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 x0).
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::inverseComposition(
        const CPosePDFGaussianInf& x1, const CPosePDFGaussianInf& x0,
        const CMatrixDouble33& COV_01)
{
        // Use implementation in CPosePDFGaussian:
        CMatrixDouble33 x1_cov(UNINITIALIZED_MATRIX), x0_cov(UNINITIALIZED_MATRIX);
        x1.cov_inv.inv(x1_cov);
        x0.cov_inv.inv(x0_cov);
 
        const CPosePDFGaussian x1_(x1.mean, x1_cov);
        const CPosePDFGaussian x0_(x0.mean, x0_cov);
 
        CPosePDFGaussian RET;
        RET.inverseComposition(x1_, x0_, COV_01);
 
        // Copy result to "this":
        this->mean = RET.mean;
        RET.cov.inv(this->cov_inv);
}
 
/*---------------------------------------------------------------
                                                        +=
 ---------------------------------------------------------------*/
void CPosePDFGaussianInf::operator+=(const CPosePDFGaussianInf& Ap)
{
        // COV:
        CMatrixDouble33 OLD_COV(UNINITIALIZED_MATRIX);
        this->cov_inv.inv(OLD_COV);
 
        CMatrixDouble33 df_dx, df_du;
 
        CPosePDF::jacobiansPoseComposition(
                this->mean,  // x
                Ap.mean,  // u
                df_dx, df_du);
 
        // this->cov = H1*this->cov*~H1 + H2*Ap.cov*~H2;
        CMatrixDouble33 cov;
 
        CMatrixDouble33 Ap_cov(UNINITIALIZED_MATRIX);
        Ap.cov_inv.inv(Ap_cov);
 
        df_dx.multiply_HCHt(OLD_COV, cov);
        df_du.multiply_HCHt(Ap_cov, cov, true);  // Accumulate result
 
        cov.inv_fast(this->cov_inv);
 
        // MEAN:
        this->mean = this->mean + Ap.mean;
}
 
bool mrpt::poses::operator==(
        const CPosePDFGaussianInf& p1, const CPosePDFGaussianInf& p2)
{
        return p1.mean == p2.mean && p1.cov_inv == p2.cov_inv;
}
 
/** Pose compose operator: RES = A (+) B , computing both the mean and the
 * covariance */
CPosePDFGaussianInf mrpt::poses::operator+(
        const CPosePDFGaussianInf& a, const CPosePDFGaussianInf& b)
{
        CPosePDFGaussianInf res(a);
        res += b;
        return res;
}
 
/** Pose inverse compose operator: RES = A (-) B , computing both the mean and
 * the covariance */
CPosePDFGaussianInf mrpt::poses::operator-(
        const CPosePDFGaussianInf& a, const CPosePDFGaussianInf& b)
{
        CPosePDFGaussianInf res;
        res.inverseComposition(a, b);
        return res;
}