CPosePDFGaussianInf.cpp

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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    |
   +---------------------------------------------------------------------------+ */
 
#include "base-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/utils/CStream.h>
 
#include <mrpt/random.h>
 
using namespace mrpt;
using namespace mrpt::utils;
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()
{
}
 
/*---------------------------------------------------------------
                                                writeToStream
  ---------------------------------------------------------------*/
void  CPosePDFGaussianInf::writeToStream(mrpt::utils::CStream &out,int *version) const
{
        if (version)
                *version = 0;
        else
        {
                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);
        }
}
 
/*---------------------------------------------------------------
                                                readFromStream
  ---------------------------------------------------------------*/
void  CPosePDFGaussianInf::readFromStream(mrpt::utils::CStream &in,int 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);
        }
}
 
 
/*---------------------------------------------------------------
 
  ---------------------------------------------------------------*/
void  CPosePDFGaussianInf::saveToTextFile(const std::string &file) const
{
        FILE    *f=os::fopen(file.c_str(),"wt");
        if (!f) return;
 
        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);
}
 
/*---------------------------------------------------------------
                                                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);
 
        MRPT_ALIGN16 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;
        randomGenerator.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;
 
        randomGenerator.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:
        MRPT_ALIGN16 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;
}