CPose3DPDFParticles.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */

#include "poses-precomp.h"  // Precompiled headers

#include <mrpt/math/TPose3D.h>
#include <mrpt/math/wrap2pi.h>
#include <mrpt/poses/CPose3D.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/poses/CPose3DPDFParticles.h>
#include <mrpt/poses/SO_SE_average.h>
#include <mrpt/random.h>
#include <mrpt/serialization/CArchive.h>
#include <mrpt/system/os.h>

using namespace mrpt;
using namespace mrpt::poses;
using namespace mrpt::math;

IMPLEMENTS_SERIALIZABLE(CPose3DPDFParticles, CPose3DPDF, mrpt::poses)

CPose3DPDFParticles::CPose3DPDFParticles(size_t M)
{
    m_particles.resize(M);
    TPose3D nullPose(0, 0, 0, 0, 0, 0);
    resetDeterministic(nullPose);
}

void CPose3DPDFParticles::copyFrom(const CPose3DPDF& o)
{
    MRPT_START
    if (this == &o) return;  // It may be used sometimes
    if (o.GetRuntimeClass() == CLASS_ID(CPose3DPDFParticles))
    {
        const auto* pdf = dynamic_cast<const CPose3DPDFParticles*>(&o);
        ASSERT_(pdf);
        m_particles = pdf->m_particles;
    }
    else if (o.GetRuntimeClass() == CLASS_ID(CPose3DPDFGaussian))
    {
        THROW_EXCEPTION("TO DO!!");
    }
    MRPT_END
}

void CPose3DPDFParticles::getMean(CPose3D& p) const
{
    MRPT_START
    // Default to (0,..,0)
    p = CPose3D();
    if (m_particles.empty()) return;

    // Calc average on SE(3)
    mrpt::poses::SE_average<3> se_averager;
    for (const auto& part : m_particles)
    {
        const double w = exp(part.log_w);
        se_averager.append(part.d, w);
    }
    se_averager.get_average(p);
    MRPT_END
}

std::tuple<CMatrixDouble66, CPose3D> CPose3DPDFParticles::getCovarianceAndMean()
    const
{
    MRPT_START

    CMatrixDouble66 cov;
    CPose3D mean;

    getMean(mean);  // First! the mean value:

    // Now the covariance:
    cov.setZero();
    CVectorFixedDouble<6> vars;
    vars.setZero();  // The diagonal of the final covariance matrix

    // Elements off the diagonal of the covariance matrix:
    double std_xy = 0, std_xz = 0, std_xya = 0, std_xp = 0, std_xr = 0;
    double std_yz = 0, std_yya = 0, std_yp = 0, std_yr = 0;
    double std_zya = 0, std_zp = 0, std_zr = 0;
    double std_yap = 0, std_yar = 0;
    double std_pr = 0;

    // Mean values in [0, 2pi] range:
    double mean_yaw = mean.yaw();
    double mean_pitch = mean.pitch();
    double mean_roll = mean.roll();
    if (mean_yaw < 0) mean_yaw += M_2PI;
    if (mean_pitch < 0) mean_pitch += M_2PI;
    if (mean_roll < 0) mean_roll += M_2PI;

    // Enought information to estimate the covariance?
    if (m_particles.size() < 2) return {cov, mean};

    // Sum all weight values:
    double W = 0;
    for (const auto& p : m_particles) W += exp(p.log_w);

    ASSERT_(W > 0);

    // Compute covariance:
    for (const auto& p : m_particles)
    {
        double w = exp(p.log_w) / W;

        // Manage 1 PI range:
        double err_yaw = wrapToPi(std::abs(p.d.yaw - mean_yaw));
        double err_pitch = wrapToPi(std::abs(p.d.pitch - mean_pitch));
        double err_roll = wrapToPi(std::abs(p.d.roll - mean_roll));

        double err_x = p.d.x - mean.x();
        double err_y = p.d.y - mean.y();
        double err_z = p.d.z - mean.z();

        vars[0] += square(err_x) * w;
        vars[1] += square(err_y) * w;
        vars[2] += square(err_z) * w;
        vars[3] += square(err_yaw) * w;
        vars[4] += square(err_pitch) * w;
        vars[5] += square(err_roll) * w;

        std_xy += err_x * err_y * w;
        std_xz += err_x * err_z * w;
        std_xya += err_x * err_yaw * w;
        std_xp += err_x * err_pitch * w;
        std_xr += err_x * err_roll * w;

        std_yz += err_y * err_z * w;
        std_yya += err_y * err_yaw * w;
        std_yp += err_y * err_pitch * w;
        std_yr += err_y * err_roll * w;

        std_zya += err_z * err_yaw * w;
        std_zp += err_z * err_pitch * w;
        std_zr += err_z * err_roll * w;

        std_yap += err_yaw * err_pitch * w;
        std_yar += err_yaw * err_roll * w;

        std_pr += err_pitch * err_roll * w;
    }  // end for it

    // Unbiased estimation of variance:
    cov(0, 0) = vars[0];
    cov(1, 1) = vars[1];
    cov(2, 2) = vars[2];
    cov(3, 3) = vars[3];
    cov(4, 4) = vars[4];
    cov(5, 5) = vars[5];

    cov(1, 0) = cov(0, 1) = std_xy;
    cov(2, 0) = cov(0, 2) = std_xz;
    cov(3, 0) = cov(0, 3) = std_xya;
    cov(4, 0) = cov(0, 4) = std_xp;
    cov(5, 0) = cov(0, 5) = std_xr;

    cov(2, 1) = cov(1, 2) = std_yz;
    cov(3, 1) = cov(1, 3) = std_yya;
    cov(4, 1) = cov(1, 4) = std_yp;
    cov(5, 1) = cov(1, 5) = std_yr;

    cov(3, 2) = cov(2, 3) = std_zya;
    cov(4, 2) = cov(2, 4) = std_zp;
    cov(5, 2) = cov(2, 5) = std_zr;

    cov(4, 3) = cov(3, 4) = std_yap;
    cov(5, 3) = cov(3, 5) = std_yar;

    cov(5, 4) = cov(4, 5) = std_pr;

    return {cov, mean};
    MRPT_END
}

uint8_t CPose3DPDFParticles::serializeGetVersion() const { return 1; }
void CPose3DPDFParticles::serializeTo(mrpt::serialization::CArchive& out) const
{
    writeParticlesToStream(out);  // v1: CPose3D -> TPose3D
}
void CPose3DPDFParticles::serializeFrom(
    mrpt::serialization::CArchive& in, uint8_t version)
{
    switch (version)
    {
        case 0:
        {
            mrpt::bayes::CParticleFilterData<
                mrpt::poses::CPose3D, PARTICLE_STORAGE>
                old;
            old.readParticlesFromStream(in);
            m_particles.clear();
            std::transform(
                old.m_particles.begin(), old.m_particles.end(),
                std::back_inserter(m_particles),
                [](const auto& p) -> CParticleData {
                    return CParticleData(p.d.asTPose(), p.log_w);
                });
        }
        break;
        case 1:
        {
            readParticlesFromStream(in);
        }
        break;
        default:
            MRPT_THROW_UNKNOWN_SERIALIZATION_VERSION(version);
    };
}

/*---------------------------------------------------------------
                        saveToTextFile
   Save PDF's m_particles to a text file. In each line it
      will go: "x y phi weight"
 ---------------------------------------------------------------*/
bool CPose3DPDFParticles::saveToTextFile(const std::string& file) const
{
    using namespace mrpt::system;

    FILE* f = os::fopen(file.c_str(), "wt");
    if (!f) return false;

    os::fprintf(f, "%% x  y  z  yaw[rad] pitch[rad] roll[rad] log_weight\n");

    for (const auto& p : m_particles)
        os::fprintf(
            f, "%f %f %f %f %f %f %e\n", p.d.x, p.d.y, p.d.z, p.d.yaw,
            p.d.pitch, p.d.roll, p.log_w);

    os::fclose(f);
    return true;
}

mrpt::math::TPose3D CPose3DPDFParticles::getParticlePose(int i) const
{
    return m_particles[i].d;
}

void CPose3DPDFParticles::changeCoordinatesReference(
    const CPose3D& newReferenceBase)
{
    for (auto& p : m_particles)
        p.d = (newReferenceBase + CPose3D(p.d)).asTPose();
}

void CPose3DPDFParticles::drawSingleSample(CPose3D& outPart) const
{
    MRPT_UNUSED_PARAM(outPart);
    THROW_EXCEPTION("TO DO!");
}

void CPose3DPDFParticles::drawManySamples(
    size_t N, std::vector<CVectorDouble>& outSamples) const
{
    MRPT_UNUSED_PARAM(N);
    MRPT_UNUSED_PARAM(outSamples);
    THROW_EXCEPTION("TO DO!");
}

void CPose3DPDFParticles::operator+=(const CPose3D& Ap)
{
    MRPT_UNUSED_PARAM(Ap);
    THROW_EXCEPTION("TO DO!");
}

void CPose3DPDFParticles::append(CPose3DPDFParticles& o)
{
    MRPT_UNUSED_PARAM(o);
    THROW_EXCEPTION("TO DO!");
}

void CPose3DPDFParticles::inverse(CPose3DPDF& o) const
{
    MRPT_START
    ASSERT_(o.GetRuntimeClass() == CLASS_ID(CPose3DPDFParticles));
    auto* out = dynamic_cast<CPose3DPDFParticles*>(&o);
    ASSERT_(out != nullptr);
    // Prepare the output:
    out->copyFrom(*this);
    const CPose3D zero(0, 0, 0);
    for (auto& p : out->m_particles) p.d = (zero - CPose3D(p.d)).asTPose();
    MRPT_END
}

TPose3D CPose3DPDFParticles::getMostLikelyParticle() const
{
    mrpt::math::TPose3D ret{0, 0, 0, 0, 0, 0};
    double max_w = -std::numeric_limits<double>::max();
    for (const auto& p : m_particles)
    {
        if (p.log_w > max_w)
        {
            ret = p.d;
            max_w = p.log_w;
        }
    }
    return ret;
}

void CPose3DPDFParticles::bayesianFusion(
    const CPose3DPDF& p1, const CPose3DPDF& p2)
{
    MRPT_UNUSED_PARAM(p1);
    MRPT_UNUSED_PARAM(p2);
    THROW_EXCEPTION("Not implemented yet!");
}

void CPose3DPDFParticles::resetDeterministic(
    const TPose3D& location, size_t particlesCount)
{
    if (particlesCount > 0) m_particles.resize(particlesCount);

    for (auto& p : m_particles)
    {
        p.d = location;
        p.log_w = 0;
    }
}

void CPose3DPDFParticles::resetUniform(
    const mrpt::math::TPose3D& cmin, const mrpt::math::TPose3D& cmax,
    const int particlesCount)
{
    MRPT_START
    if (particlesCount > 0) m_particles.resize(particlesCount);

    auto& rnd = mrpt::random::getRandomGenerator();

    for (auto& p : m_particles)
    {
        p.d.x = rnd.drawUniform(cmin.x, cmax.x);
        p.d.y = rnd.drawUniform(cmin.y, cmax.y);
        p.d.z = rnd.drawUniform(cmin.z, cmax.z);
        p.d.yaw = rnd.drawUniform(cmin.yaw, cmax.yaw);
        p.d.pitch = rnd.drawUniform(cmin.pitch, cmax.pitch);
        p.d.roll = rnd.drawUniform(cmin.roll, cmax.roll);
        p.log_w = 0;
    }
    MRPT_END
}