CBeaconMap.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2020, 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 "maps-precomp.h"  // Precomp header

#include <mrpt/bayes/CParticleFilter.h>
#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/config/CConfigFileBase.h>
#include <mrpt/core/round.h>  // round()
#include <mrpt/io/CFileOutputStream.h>
#include <mrpt/maps/CBeaconMap.h>
#include <mrpt/math/data_utils.h>  // averageLogLikelihood()
#include <mrpt/math/geometry.h>
#include <mrpt/obs/CObservationBeaconRanges.h>
#include <mrpt/opengl/CGridPlaneXY.h>
#include <mrpt/opengl/COpenGLScene.h>
#include <mrpt/opengl/CSetOfObjects.h>
#include <mrpt/opengl/stock_objects.h>
#include <mrpt/random.h>
#include <mrpt/serialization/CArchive.h>
#include <mrpt/system/os.h>
#include <mrpt/system/string_utils.h>
#include <Eigen/Dense>

using namespace mrpt;
using namespace mrpt::maps;
using namespace mrpt::math;
using namespace mrpt::obs;
using namespace mrpt::random;
using namespace mrpt::poses;
using namespace mrpt::bayes;
using namespace mrpt::system;
using namespace mrpt::tfest;
using namespace std;

//  =========== Begin of Map definition ============
MAP_DEFINITION_REGISTER(
    "mrpt::maps::CBeaconMap,beaconMap", mrpt::maps::CBeaconMap)

CBeaconMap::TMapDefinition::TMapDefinition() = default;
void CBeaconMap::TMapDefinition::loadFromConfigFile_map_specific(
    const mrpt::config::CConfigFileBase& source,
    const std::string& sectionNamePrefix)
{
    // [<sectionNamePrefix>+"_creationOpts"]
    // const std::string sSectCreation =
    // sectionNamePrefix+string("_creationOpts");
    // MRPT_LOAD_CONFIG_VAR(resolution, float,   source,sSectCreation);

    insertionOpts.loadFromConfigFile(
        source, sectionNamePrefix + string("_insertOpts"));
    likelihoodOpts.loadFromConfigFile(
        source, sectionNamePrefix + string("_likelihoodOpts"));
}

void CBeaconMap::TMapDefinition::dumpToTextStream_map_specific(
    std::ostream& out) const
{
    // LOADABLEOPTS_DUMP_VAR(resolution     , float);

    this->insertionOpts.dumpToTextStream(out);
    this->likelihoodOpts.dumpToTextStream(out);
}

mrpt::maps::CMetricMap* CBeaconMap::internal_CreateFromMapDefinition(
    const mrpt::maps::TMetricMapInitializer& _def)
{
    const CBeaconMap::TMapDefinition& def =
        *dynamic_cast<const CBeaconMap::TMapDefinition*>(&_def);
    auto* obj = new CBeaconMap();
    obj->insertionOptions = def.insertionOpts;
    obj->likelihoodOptions = def.likelihoodOpts;
    return obj;
}
//  =========== End of Map definition Block =========

IMPLEMENTS_SERIALIZABLE(CBeaconMap, CMetricMap, mrpt::maps)

/*---------------------------------------------------------------
                        Constructor
  ---------------------------------------------------------------*/
CBeaconMap::CBeaconMap() : m_beacons(), likelihoodOptions(), insertionOptions()
{
}

/*---------------------------------------------------------------
                        clear
  ---------------------------------------------------------------*/
void CBeaconMap::internal_clear() { m_beacons.clear(); }
/*---------------------------------------------------------------
                        getLandmarksCount
  ---------------------------------------------------------------*/
size_t CBeaconMap::size() const { return m_beacons.size(); }
/*---------------------------------------------------------------
    Resize
  ---------------------------------------------------------------*/
void CBeaconMap::resize(const size_t N) { m_beacons.resize(N); }
uint8_t CBeaconMap::serializeGetVersion() const { return 1; }
void CBeaconMap::serializeTo(mrpt::serialization::CArchive& out) const
{
    out << genericMapParams;  // v1

    // First, write the number of landmarks:
    const uint32_t n = m_beacons.size();
    out << n;
    // Write all landmarks:
    for (const auto& it : *this) out << it;
}

void CBeaconMap::serializeFrom(
    mrpt::serialization::CArchive& in, uint8_t version)
{
    switch (version)
    {
        case 0:
        case 1:
        {
            if (version >= 1) in >> genericMapParams;  // v1

            uint32_t n, i;

            // Delete previous content of map:
            clear();

            // Load from stream:
            // Read all landmarks:
            in >> n;
            m_beacons.resize(n);
            for (i = 0; i < n; i++) in >> m_beacons[i];
        }
        break;
        default:
            MRPT_THROW_UNKNOWN_SERIALIZATION_VERSION(version);
    };
}

/*---------------------------------------------------------------
                    computeObservationLikelihood
  ---------------------------------------------------------------*/
double CBeaconMap::internal_computeObservationLikelihood(
    const CObservation& obs, const CPose3D& robotPose3D)
{
    MRPT_START

    /* ===============================================================================================================
        Refer to the papers:
        - IROS 2008, "Efficient Probabilistic Range-Only SLAM",
            http://www.mrpt.org/paper-ro-slam-with-sog/

        - ICRA 2008, "A Pure Probabilistic Approach to Range-Only SLAM",
            http://www.mrpt.org/tutorials/slam-algorithms/rangeonly_slam/
       ===============================================================================================================
       */

    if (CLASS_ID(CObservationBeaconRanges) == obs.GetRuntimeClass())
    {
        /********************************************************************

                        OBSERVATION TYPE: CObservationBeaconRanges

                Lik. between "this" and "auxMap";

            ********************************************************************/
        double ret = 0;
        const auto& o = dynamic_cast<const CObservationBeaconRanges&>(obs);
        const CBeacon* beac;
        CPoint3D sensor3D;

        for (auto& it_obs : o.sensedData)
        {
            // Look for the beacon in this map:
            beac = getBeaconByID(it_obs.beaconID);

            if (beac != nullptr && it_obs.sensedDistance > 0 &&
                !std::isnan(it_obs.sensedDistance))
            {
                float sensedRange = it_obs.sensedDistance;

                // FOUND: Compute the likelihood function:
                // -----------------------------------------------------
                // Compute the 3D position of the sensor:
                sensor3D = robotPose3D + it_obs.sensorLocationOnRobot;

                // Depending on the PDF type of the beacon in the map:
                switch (beac->m_typePDF)
                {
                    // ------------------------------
                    // PDF is MonteCarlo
                    // ------------------------------
                    case CBeacon::pdfMonteCarlo:
                    {
                        CPointPDFParticles::CParticleList::const_iterator it;
                        CVectorDouble logWeights(
                            beac->m_locationMC.m_particles.size());
                        CVectorDouble logLiks(
                            beac->m_locationMC.m_particles.size());
                        CVectorDouble::iterator itLW, itLL;

                        for (it = beac->m_locationMC.m_particles.begin(),
                            itLW = logWeights.begin(), itLL = logLiks.begin();
                             it != beac->m_locationMC.m_particles.end();
                             ++it, ++itLW, ++itLL)
                        {
                            float expectedRange = sensor3D.distance3DTo(
                                it->d->x, it->d->y, it->d->z);
                            // expectedRange +=
                            // float(0.1*(1-exp(-0.16*expectedRange)));

                            *itLW = it->log_w;  // Linear weight of this
                            // likelihood component
                            *itLL = -0.5 * square(
                                               (sensedRange - expectedRange) /
                                               likelihoodOptions.rangeStd);
                            // ret+= exp(
                            // -0.5*square((sensedRange-expectedRange)/likelihoodOptions.rangeStd)
                            // );
                        }  // end for it

                        if (logWeights.size())
                            ret += math::averageLogLikelihood(
                                logWeights, logLiks);  // A numerically-stable
                        // method to average the
                        // likelihoods
                    }
                    break;
                    // ------------------------------
                    // PDF is Gaussian
                    // ------------------------------
                    case CBeacon::pdfGauss:
                    {
                        // Compute the Jacobian H and varZ
                        CMatrixFixed<double, 1, 3> H;
                        float varZ, varR = square(likelihoodOptions.rangeStd);
                        float Ax =
                            beac->m_locationGauss.mean.x() - sensor3D.x();
                        float Ay =
                            beac->m_locationGauss.mean.y() - sensor3D.y();
                        float Az =
                            beac->m_locationGauss.mean.z() - sensor3D.z();
                        H(0, 0) = Ax;
                        H(0, 1) = Ay;
                        H(0, 2) = Az;
                        float expectedRange =
                            sensor3D.distanceTo(beac->m_locationGauss.mean);
                        H.asEigen() *=
                            1.0 / expectedRange;  // sqrt(Ax*Ax+Ay*Ay+Az*Az);

                        varZ = mrpt::math::multiply_HCHt_scalar(
                            H, beac->m_locationGauss.cov);

                        varZ += varR;

                        // Compute the mean expected range (add bias!):
                        // expectedRange +=
                        // float(0.1*(1-exp(-0.16*expectedRange)));

                        // Compute the likelihood:
                        //   lik \propto exp( -0.5* ( ^z - z  )^2 / varZ );
                        //   log_lik = -0.5* ( ^z - z  )^2 / varZ
                        ret +=
                            -0.5 * square(sensedRange - expectedRange) / varZ;
                    }
                    break;
                    // ------------------------------
                    // PDF is SOG
                    // ------------------------------
                    case CBeacon::pdfSOG:
                    {
                        CMatrixDouble13 H;
                        CVectorDouble logWeights(beac->m_locationSOG.size());
                        CVectorDouble logLiks(beac->m_locationSOG.size());
                        CVectorDouble::iterator itLW, itLL;
                        CPointPDFSOG::const_iterator it;
                        // For each Gaussian mode:
                        for (it = beac->m_locationSOG.begin(),
                            itLW = logWeights.begin(), itLL = logLiks.begin();
                             it != beac->m_locationSOG.end();
                             ++it, ++itLW, ++itLL)
                        {
                            // Compute the Jacobian H and varZ
                            double varZ,
                                varR = square(likelihoodOptions.rangeStd);
                            double Ax = it->val.mean.x() - sensor3D.x();
                            double Ay = it->val.mean.y() - sensor3D.y();
                            double Az = it->val.mean.z() - sensor3D.z();
                            H(0, 0) = Ax;
                            H(0, 1) = Ay;
                            H(0, 2) = Az;
                            double expectedRange =
                                sensor3D.distanceTo(it->val.mean);
                            H.asEigen() *=
                                1.0 /
                                expectedRange;  // sqrt(Ax*Ax+Ay*Ay+Az*Az);

                            varZ = mrpt::math::multiply_HCHt_scalar(
                                H, it->val.cov);
                            varZ += varR;

                            // Compute the mean expected range (add bias!):
                            // expectedRange +=
                            // float(0.1*(1-exp(-0.16*expectedRange)));

                            // Compute the likelihood:
                            *itLW = it->log_w;  // log-weight of this likelihood
                            // component
                            *itLL = -0.5 * square(sensedRange - expectedRange) /
                                    varZ;
                        }  // end for each mode

                        // Accumulate to the overall (log) likelihood value:
                        if (logWeights.size())
                            ret += math::averageLogLikelihood(
                                logWeights,
                                logLiks);  // log( linear_lik / sumW );
                    }
                    break;

                    default:
                        THROW_EXCEPTION("Invalid beac->m_typePDF!!!");
                };
            }
            else
            {
                // If not found, a uniform distribution:
                if (o.maxSensorDistance != o.minSensorDistance)
                    ret +=
                        log(1.0 / (o.maxSensorDistance - o.minSensorDistance));
            }
        }  // for each sensed beacon "it"

        // printf("ret: %e\n",ret);
        MRPT_CHECK_NORMAL_NUMBER(ret);
        return ret;

    }  // end of likelihood of CObservationBeaconRanges
    else
    {
        /********************************************************************
                    OBSERVATION TYPE: Unknown
        ********************************************************************/
        return 0;
    }
    MRPT_END
}

/*---------------------------------------------------------------
                        insertObservation
  ---------------------------------------------------------------*/
bool CBeaconMap::internal_insertObservation(
    const mrpt::obs::CObservation& obs, const CPose3D* robotPose)
{
    MRPT_START

    CPose2D robotPose2D;
    CPose3D robotPose3D;

    if (robotPose)
    {
        robotPose2D = CPose2D(*robotPose);
        robotPose3D = (*robotPose);
    }
    else
    {
        // Default values are (0,0,0)
    }

    if (CLASS_ID(CObservationBeaconRanges) == obs.GetRuntimeClass())
    {
        /********************************************************************
                        OBSERVATION TYPE: CObservationBeaconRanges
         ********************************************************************/

        /* ===============================================================================================================
           Refer to the papers:
           - IROS 2008, "Efficient Probabilistic Range-Only SLAM",
               https://www.mrpt.org/paper-ro-slam-with-sog/

           - ICRA 2008, "A Pure Probabilistic Approach to Range-Only SLAM",
               https://www.mrpt.org/tutorials/slam-algorithms/rangeonly_slam/
          ===============================================================================================================
          */

        // Here we fuse OR create the beacon position PDF:
        // --------------------------------------------------------
        const auto& o = static_cast<const CObservationBeaconRanges&>(obs);

        for (const auto& it : o.sensedData)
        {
            CPoint3D sensorPnt(robotPose3D + it.sensorLocationOnRobot);
            float sensedRange = it.sensedDistance;
            unsigned int sensedID = it.beaconID;

            CBeacon* beac = getBeaconByID(sensedID);

            if (sensedRange > 0)  // Only sensible range values!
            {
                if (!beac)
                {
                    // ======================================
                    //                INSERT
                    // ======================================
                    CBeacon newBeac;
                    newBeac.m_ID = sensedID;

                    if (insertionOptions.insertAsMonteCarlo)
                    {
                        // Insert as a new set of samples:
                        // ------------------------------------------------

                        newBeac.m_typePDF = CBeacon::pdfMonteCarlo;

                        size_t numParts = round(
                            insertionOptions.MC_numSamplesPerMeter *
                            sensedRange);
                        ASSERT_(
                            insertionOptions.minElevation_deg <=
                            insertionOptions.maxElevation_deg);
                        double minA =
                            DEG2RAD(insertionOptions.minElevation_deg);
                        double maxA =
                            DEG2RAD(insertionOptions.maxElevation_deg);
                        newBeac.m_locationMC.setSize(numParts);
                        for (auto& m_particle :
                             newBeac.m_locationMC.m_particles)
                        {
                            double th =
                                getRandomGenerator().drawUniform(-M_PI, M_PI);
                            double el =
                                getRandomGenerator().drawUniform(minA, maxA);
                            double R = getRandomGenerator().drawGaussian1D(
                                sensedRange, likelihoodOptions.rangeStd);
                            m_particle.d->x =
                                sensorPnt.x() + R * cos(th) * cos(el);
                            m_particle.d->y =
                                sensorPnt.y() + R * sin(th) * cos(el);
                            m_particle.d->z = sensorPnt.z() + R * sin(el);
                        }  // end for itP
                    }
                    else
                    {
                        // Insert as a Sum of Gaussians:
                        // ------------------------------------------------
                        newBeac.m_typePDF = CBeacon::pdfSOG;
                        CBeacon::generateRingSOG(
                            sensedRange,  // Sensed range
                            newBeac.m_locationSOG,  // Output SOG
                            this,  // My CBeaconMap, for options.
                            sensorPnt  // Sensor point
                        );
                    }

                    // and insert it:
                    m_beacons.push_back(newBeac);

                }  // end insert
                else
                {
                    // ======================================
                    //                  FUSE
                    // ======================================
                    switch (beac->m_typePDF)
                    {
                        // ------------------------------
                        // FUSE: PDF is MonteCarlo
                        // ------------------------------
                        case CBeacon::pdfMonteCarlo:
                        {
                            double maxW = -1e308, sumW = 0;
                            // Update weights:
                            // --------------------
                            for (auto& p : beac->m_locationMC.m_particles)
                            {
                                float expectedRange = sensorPnt.distance3DTo(
                                    p.d->x, p.d->y, p.d->z);
                                p.log_w +=
                                    -0.5 * square(
                                               (sensedRange - expectedRange) /
                                               likelihoodOptions.rangeStd);
                                maxW = max(p.log_w, maxW);
                                sumW += exp(p.log_w);
                            }

                            // Perform resampling (SIR filter) or not (simply
                            // accumulate weights)??
                            // ------------------------------------------------------------------------
                            if (insertionOptions.MC_performResampling)
                            {
                                // Yes, perform an auxiliary PF SIR here:
                                // ---------------------------------------------
                                if (beac->m_locationMC.ESS() < 0.5)
                                {
                                    // We must resample:
                                    // Make a list with the log weights:
                                    vector<double> log_ws;
                                    vector<size_t> indxs;
                                    beac->m_locationMC.getWeights(log_ws);

                                    // And compute the resampled indexes:
                                    CParticleFilterCapable::computeResampling(
                                        CParticleFilter::prSystematic, log_ws,
                                        indxs);

                                    // Replace with the new samples:
                                    beac->m_locationMC.performSubstitution(
                                        indxs);

                                    // Determine if this is a 2D beacon map:
                                    bool is2D =
                                        (insertionOptions.minElevation_deg ==
                                         insertionOptions.maxElevation_deg);
                                    float noiseStd =
                                        insertionOptions
                                            .MC_afterResamplingNoise;

                                    // AND, add a small noise:
                                    CPointPDFParticles::CParticleList::iterator
                                        itSample;
                                    for (itSample = beac->m_locationMC
                                                        .m_particles.begin();
                                         itSample !=
                                         beac->m_locationMC.m_particles.end();
                                         ++itSample)
                                    {
                                        itSample->d->x +=
                                            getRandomGenerator().drawGaussian1D(
                                                0, noiseStd);
                                        itSample->d->y +=
                                            getRandomGenerator().drawGaussian1D(
                                                0, noiseStd);
                                        if (!is2D)
                                            itSample->d->z +=
                                                getRandomGenerator()
                                                    .drawGaussian1D(
                                                        0, noiseStd);
                                    }
                                }
                            }  // end "do resample"
                            else
                            {
                                // Do not resample:
                                // ---------------------------------------------

                                // Remove very very very unlikely particles:
                                // -------------------------------------------
                                for (auto it_p =
                                         beac->m_locationMC.m_particles.begin();
                                     it_p !=
                                     beac->m_locationMC.m_particles.end();)
                                {
                                    if (it_p->log_w <
                                        (maxW - insertionOptions
                                                    .MC_thresholdNegligible))
                                    {
                                        it_p->d.reset();
                                        it_p = beac->m_locationMC.m_particles
                                                   .erase(it_p);
                                    }
                                    else
                                        ++it_p;
                                }
                            }  // end "do not resample"

                            // Normalize weights:
                            //  log_w = log( exp(log_w)/sumW ) ->
                            //  log_w -= log(sumW);
                            // -----------------------------------------
                            sumW = log(sumW);
                            for (auto& p : beac->m_locationMC.m_particles)
                                p.log_w -= sumW;

                            // Is the moment to turn into a Gaussian??
                            // -------------------------------------------
                            auto [COV, MEAN] =
                                beac->m_locationMC.getCovarianceAndMean();

                            double D1 = sqrt(COV(0, 0));
                            double D2 = sqrt(COV(1, 1));
                            double D3 = sqrt(COV(2, 2));

                            double mxVar = max3(D1, D2, D3);

                            if (mxVar < insertionOptions.MC_maxStdToGauss)
                            {
                                // Collapse into Gaussian:
                                beac->m_locationMC
                                    .clear();  // Erase prev. samples

                                // Assure a non-null covariance!
                                CMatrixDouble COV2 = CMatrixDouble(COV);
                                COV2.setSize(2, 2);
                                if (COV2.det() == 0)
                                {
                                    COV.setIdentity();
                                    COV *= square(0.01f);
                                    if (insertionOptions.minElevation_deg ==
                                        insertionOptions.maxElevation_deg)
                                        COV(2, 2) = 0;  // We are in a 2D map:
                                }

                                beac->m_typePDF =
                                    CBeacon::pdfGauss;  // Pass to gaussian.
                                beac->m_locationGauss.mean = MEAN;
                                beac->m_locationGauss.cov = COV;
                            }
                        }
                        break;

                        // ------------------------------
                        // FUSE: PDF is Gaussian:
                        // ------------------------------
                        case CBeacon::pdfGauss:
                        {
                            // Compute the mean expected range:
                            float expectedRange = sensorPnt.distanceTo(
                                beac->m_locationGauss.mean);
                            float varR = square(likelihoodOptions.rangeStd);
                            // bool useEKF_or_KF = true;

                            // if (useEKF_or_KF)
                            {
                                // EKF method:
                                // ---------------------
                                // Add bias:
                                // expectedRange +=
                                // float(0.1*(1-exp(-0.16*expectedRange)));

                                // An EKF for updating the Gaussian:
                                float y = sensedRange - expectedRange;

                                // Compute the Jacobian H and varZ
                                CMatrixDouble13 H;
                                double varZ;
                                double Ax =
                                    (beac->m_locationGauss.mean.x() -
                                     sensorPnt.x());
                                double Ay =
                                    (beac->m_locationGauss.mean.y() -
                                     sensorPnt.y());
                                double Az =
                                    (beac->m_locationGauss.mean.z() -
                                     sensorPnt.z());
                                H(0, 0) = Ax;
                                H(0, 1) = Ay;
                                H(0, 2) = Az;
                                H.asEigen() *=
                                    1.0 /
                                    expectedRange;  // sqrt(Ax*Ax+Ay*Ay+Az*Az);
                                varZ = mrpt::math::multiply_HCHt_scalar(
                                    H, beac->m_locationGauss.cov);
                                varZ += varR;

                                Eigen::Vector3d K =
                                    beac->m_locationGauss.cov.asEigen() *
                                    H.transpose();
                                K *= 1.0 / varZ;

                                // Update stage of the EKF:
                                beac->m_locationGauss.mean.x_incr(K(0, 0) * y);
                                beac->m_locationGauss.mean.y_incr(K(1, 0) * y);
                                beac->m_locationGauss.mean.z_incr(K(2, 0) * y);

                                beac->m_locationGauss.cov =
                                    ((Eigen::Matrix<double, 3, 3>::Identity() -
                                      K * H.asEigen()) *
                                     beac->m_locationGauss.cov.asEigen())
                                        .eval();
                            }
                        }
                        break;
                        // ------------------------------
                        // FUSE: PDF is SOG
                        // ------------------------------
                        case CBeacon::pdfSOG:
                        {
                            // Compute the mean expected range for this mode:
                            float varR = square(likelihoodOptions.rangeStd);

                            // For each Gaussian mode:
                            //  1) Update its weight (using the likelihood of
                            //  the observation linearized at the mean)
                            //  2) Update its mean/cov (as in the simple EKF)
                            double max_w = -1e9;
                            for (auto& mode : beac->m_locationSOG)
                            {
                                double expectedRange =
                                    sensorPnt.distanceTo(mode.val.mean);

                                // An EKF for updating the Gaussian:
                                double y = sensedRange - expectedRange;

                                // Compute the Jacobian H and varZ
                                CMatrixDouble13 H;
                                double varZ;
                                double Ax = (mode.val.mean.x() - sensorPnt.x());
                                double Ay = (mode.val.mean.y() - sensorPnt.y());
                                double Az = (mode.val.mean.z() - sensorPnt.z());
                                H(0, 0) = Ax;
                                H(0, 1) = Ay;
                                H(0, 2) = Az;
                                H.asEigen() *= 1.0 / expectedRange;
                                varZ = mrpt::math::multiply_HCHt_scalar(
                                    H, mode.val.cov);
                                varZ += varR;
                                Eigen::Vector3d K =
                                    mode.val.cov.asEigen() * H.transpose();
                                K *= 1.0 / varZ;

                                // Update stage of the EKF:
                                mode.val.mean.x_incr(K(0, 0) * y);
                                mode.val.mean.y_incr(K(1, 0) * y);
                                mode.val.mean.z_incr(K(2, 0) * y);

                                mode.val.cov = (Eigen::Matrix3d::Identity() -
                                                K * H.asEigen()) *
                                               mode.val.cov.asEigen();

                                // Update the weight of this mode:
                                // ----------------------------------
                                mode.log_w += -0.5 * square(y) / varZ;

                                // keep the maximum mode weight
                                max_w = max(max_w, mode.log_w);
                            }  // end for each mode

                            // Remove modes with negligible weights:
                            // -----------------------------------------------------------
                            for (auto it_p = beac->m_locationSOG.begin();
                                 it_p != beac->m_locationSOG.end();)
                            {
                                if (max_w - it_p->log_w >
                                    insertionOptions.SOG_thresholdNegligible)
                                    it_p = beac->m_locationSOG.erase(it_p);
                                else
                                    ++it_p;
                            }

                            // Normalize the weights:
                            beac->m_locationSOG.normalizeWeights();

                            // Should we pass this beacon to a single Gaussian
                            // mode?
                            // -----------------------------------------------------------
                            const auto [curCov, curMean] =
                                beac->m_locationSOG.getCovarianceAndMean();

                            double D1 = sqrt(curCov(0, 0));
                            double D2 = sqrt(curCov(1, 1));
                            double D3 = sqrt(curCov(2, 2));
                            float maxDiag = max3(D1, D2, D3);

                            if (maxDiag < 0.10f)
                            {
                                // Yes, transform:
                                beac->m_locationGauss.mean = curMean;
                                beac->m_locationGauss.cov = curCov;
                                beac->m_typePDF = CBeacon::pdfGauss;
                                // Done!
                            }
                        }
                        break;
                        default:
                            THROW_EXCEPTION("Invalid beac->m_typePDF!!!");
                    };

                }  // end fuse
            }  // end if range makes sense
        }  // end for each observation

        // DONE!!
        // Observation was successfully inserted into the map
        return true;
    }
    else
    {
        return false;
    }

    MRPT_END
}

/*---------------------------------------------------------------
                determineMatching2D
  ---------------------------------------------------------------*/
void CBeaconMap::determineMatching2D(
    const mrpt::maps::CMetricMap* otherMap, const CPose2D& otherMapPose,
    TMatchingPairList& correspondences,
    [[maybe_unused]] const TMatchingParams& params,
    TMatchingExtraResults& extraResults) const
{
    MRPT_START
    extraResults = TMatchingExtraResults();

    CBeaconMap auxMap;
    CPose3D otherMapPose3D(otherMapPose);

    // Check the other map class:
    ASSERT_(otherMap->GetRuntimeClass() == CLASS_ID(CBeaconMap));
    const auto* otherMap2 = dynamic_cast<const CBeaconMap*>(otherMap);
    vector<bool> otherCorrespondences;

    // Coordinates change:
    auxMap.changeCoordinatesReference(otherMapPose3D, otherMap2);

    // Use the 3D matching method:
    computeMatchingWith3DLandmarks(
        otherMap2, correspondences, extraResults.correspondencesRatio,
        otherCorrespondences);

    MRPT_END
}

/*---------------------------------------------------------------
                changeCoordinatesReference
  ---------------------------------------------------------------*/
void CBeaconMap::changeCoordinatesReference(const CPose3D& newOrg)
{
    // Change the reference of each individual beacon:
    for (auto& m_beacon : m_beacons)
        m_beacon.changeCoordinatesReference(newOrg);
}

/*---------------------------------------------------------------
                changeCoordinatesReference
  ---------------------------------------------------------------*/
void CBeaconMap::changeCoordinatesReference(
    const CPose3D& newOrg, const mrpt::maps::CBeaconMap* otherMap)
{
    // In this object we cannot apply any special speed-up: Just copy and change
    // coordinates:
    (*this) = *otherMap;
    changeCoordinatesReference(newOrg);
}

/*---------------------------------------------------------------
                        computeMatchingWith3DLandmarks
  ---------------------------------------------------------------*/
void CBeaconMap::computeMatchingWith3DLandmarks(
    const mrpt::maps::CBeaconMap* anotherMap,
    TMatchingPairList& correspondences, float& correspondencesRatio,
    vector<bool>& otherCorrespondences) const
{
    MRPT_START

    TSequenceBeacons::const_iterator thisIt, otherIt;
    size_t nThis, nOther;
    unsigned int j, k;
    TMatchingPair match;
    CPointPDFGaussian pointPDF_k, pointPDF_j;
    vector<bool> thisLandmarkAssigned;

    // Get the number of landmarks:
    nThis = m_beacons.size();
    nOther = anotherMap->m_beacons.size();

    // Initially no LM has a correspondence:
    thisLandmarkAssigned.resize(nThis, false);

    // Initially, set all landmarks without correspondences:
    correspondences.clear();
    otherCorrespondences.clear();
    otherCorrespondences.resize(nOther, false);
    correspondencesRatio = 0;

    for (k = 0, otherIt = anotherMap->m_beacons.begin();
         otherIt != anotherMap->m_beacons.end(); ++otherIt, ++k)
    {
        for (j = 0, thisIt = m_beacons.begin(); thisIt != m_beacons.end();
             ++thisIt, ++j)
        {
            // Is it a correspondence?
            if ((otherIt)->m_ID == (thisIt)->m_ID)
            {
                // If a previous correspondence for this LM was found, discard
                // this one!
                if (!thisLandmarkAssigned[j])
                {
                    thisLandmarkAssigned[j] = true;

                    // OK: A correspondence found!!
                    otherCorrespondences[k] = true;

                    match.this_idx = j;

                    CPoint3D mean_j = m_beacons[j].getMeanVal();

                    match.this_x = mean_j.x();
                    match.this_y = mean_j.y();
                    match.this_z = mean_j.z();

                    CPoint3D mean_k = anotherMap->m_beacons[k].getMeanVal();
                    match.other_idx = k;
                    match.other_x = mean_k.x();
                    match.other_y = mean_k.y();
                    match.other_z = mean_k.z();

                    correspondences.push_back(match);
                }
            }

        }  // end of "otherIt" is SIFT

    }  // end of other it., k

    // Compute the corrs ratio:
    correspondencesRatio = 2.0f * correspondences.size() / d2f(nThis + nOther);

    MRPT_END
}

/*---------------------------------------------------------------
                        saveToMATLABScript3D
  ---------------------------------------------------------------*/
bool CBeaconMap::saveToMATLABScript3D(
    const string& file, [[maybe_unused]] const char* style,
    [[maybe_unused]] float confInterval) const
{
    FILE* f = os::fopen(file.c_str(), "wt");
    if (!f) return false;

    // Header:
    os::fprintf(
        f, "%%-------------------------------------------------------\n");
    os::fprintf(f, "%% File automatically generated using the MRPT method:\n");
    os::fprintf(f, "%%   'CBeaconMap::saveToMATLABScript3D'\n");
    os::fprintf(f, "%%\n");
    os::fprintf(f, "%%                        ~ MRPT ~\n");
    os::fprintf(
        f, "%%  Jose Luis Blanco Claraco, University of Malaga @ 2006\n");
    os::fprintf(f, "%%  http://www.isa.uma.es/ \n");
    os::fprintf(
        f, "%%-------------------------------------------------------\n\n");

    // Main code:
    os::fprintf(f, "hold on;\n\n");
    std::vector<std::string> strs;
    string s;

    for (const auto& m_beacon : m_beacons)
    {
        m_beacon.getAsMatlabDrawCommands(strs);
        mrpt::system::stringListAsString(strs, s);
        os::fprintf(f, "%s", s.c_str());
    }

    os::fprintf(f, "axis equal;grid on;");

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

void CBeaconMap::TLikelihoodOptions::dumpToTextStream(std::ostream& out) const
{
    out << "\n----------- [CBeaconMap::TLikelihoodOptions] ------------ \n\n";
    out << mrpt::format(
        "rangeStd                                = %f\n", rangeStd);
    out << "\n";
}

/*---------------------------------------------------------------
                    loadFromConfigFile
  ---------------------------------------------------------------*/
void CBeaconMap::TLikelihoodOptions::loadFromConfigFile(
    const mrpt::config::CConfigFileBase& iniFile, const string& section)
{
    rangeStd = iniFile.read_float(section.c_str(), "rangeStd", rangeStd);
}

void CBeaconMap::TInsertionOptions::dumpToTextStream(std::ostream& out) const
{
    out << "\n----------- [CBeaconMap::TInsertionOptions] ------------ \n\n";

    out << mrpt::format(
        "insertAsMonteCarlo                      = %c\n",
        insertAsMonteCarlo ? 'Y' : 'N');
    out << mrpt::format(
        "minElevation_deg                        = %.03f\n", minElevation_deg);
    out << mrpt::format(
        "maxElevation_deg                        = %.03f\n", maxElevation_deg);
    out << mrpt::format(
        "MC_numSamplesPerMeter                   = %d\n",
        MC_numSamplesPerMeter);
    out << mrpt::format(
        "MC_maxStdToGauss                        = %.03f\n", MC_maxStdToGauss);
    out << mrpt::format(
        "MC_thresholdNegligible                  = %.03f\n",
        MC_thresholdNegligible);
    out << mrpt::format(
        "MC_performResampling                    = %c\n",
        MC_performResampling ? 'Y' : 'N');
    out << mrpt::format(
        "MC_afterResamplingNoise                 = %.03f\n",
        MC_afterResamplingNoise);
    out << mrpt::format(
        "SOG_thresholdNegligible                 = %.03f\n",
        SOG_thresholdNegligible);
    out << mrpt::format(
        "SOG_maxDistBetweenGaussians             = %.03f\n",
        SOG_maxDistBetweenGaussians);
    out << mrpt::format(
        "SOG_separationConstant                  = %.03f\n",
        SOG_separationConstant);

    out << "\n";
}

/*---------------------------------------------------------------
                    loadFromConfigFile
  ---------------------------------------------------------------*/
void CBeaconMap::TInsertionOptions::loadFromConfigFile(
    const mrpt::config::CConfigFileBase& iniFile, const string& section)
{
    MRPT_LOAD_CONFIG_VAR(insertAsMonteCarlo, bool, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(maxElevation_deg, double, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(minElevation_deg, double, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(MC_numSamplesPerMeter, int, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(MC_maxStdToGauss, float, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(
        MC_thresholdNegligible, double, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(MC_performResampling, bool, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(
        MC_afterResamplingNoise, float, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(
        SOG_thresholdNegligible, float, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(
        SOG_maxDistBetweenGaussians, float, iniFile, section.c_str());
    MRPT_LOAD_CONFIG_VAR(
        SOG_separationConstant, float, iniFile, section.c_str());
}

/*---------------------------------------------------------------
                     isEmpty
  ---------------------------------------------------------------*/
bool CBeaconMap::isEmpty() const { return size() == 0; }
/*---------------------------------------------------------------
                     simulateBeaconReadings
  ---------------------------------------------------------------*/
void CBeaconMap::simulateBeaconReadings(
    const CPose3D& in_robotPose, const CPoint3D& in_sensorLocationOnRobot,
    CObservationBeaconRanges& out_Observations) const
{
    TSequenceBeacons::const_iterator it;
    mrpt::obs::CObservationBeaconRanges::TMeasurement newMeas;
    CPoint3D point3D, beacon3D;
    CPointPDFGaussian beaconPDF;

    // Compute the 3D position of the sensor:
    point3D = in_robotPose + in_sensorLocationOnRobot;

    // Clear output data:
    out_Observations.sensedData.clear();

    // For each BEACON landmark in the map:
    for (it = m_beacons.begin(); it != m_beacons.end(); ++it)
    {
        it->getMean(beacon3D);

        float range = point3D.distanceTo(beacon3D);

        if (range < out_Observations.maxSensorDistance &&
            range > out_Observations.minSensorDistance)
        {
            // Add noise:
            range += getRandomGenerator().drawGaussian1D(
                0, out_Observations.stdError);

            // Fill out:
            newMeas.beaconID = it->m_ID;
            newMeas.sensorLocationOnRobot = in_sensorLocationOnRobot;
            newMeas.sensedDistance = range;

            // Insert:
            out_Observations.sensedData.push_back(newMeas);
        }
    }  // end for it
    // Done!
}

/*---------------------------------------------------------------
                     saveMetricMapRepresentationToFile
  ---------------------------------------------------------------*/
void CBeaconMap::saveMetricMapRepresentationToFile(
    const string& filNamePrefix) const
{
    MRPT_START

    // Matlab:
    string fil1(filNamePrefix + string("_3D.m"));
    saveToMATLABScript3D(fil1);

    // 3D Scene:
    opengl::COpenGLScene scene;
    opengl::CSetOfObjects::Ptr obj3D =
        std::make_shared<opengl::CSetOfObjects>();

    getAs3DObject(obj3D);
    auto objGround = opengl::CGridPlaneXY::Create(
        -100.0f, 100.0f, -100.0f, 100.0f, .0f, 1.f);

    scene.insert(obj3D);
    scene.insert(objGround);

    string fil2(filNamePrefix + string("_3D.3Dscene"));
    scene.saveToFile(fil2);

    // Textual representation:
    string fil3(filNamePrefix + string("_covs.txt"));
    saveToTextFile(fil3);

    // Total number of particles / modes:
    string fil4(filNamePrefix + string("_population.txt"));
    {
        FILE* f = os::fopen(fil4.c_str(), "wt");
        if (f)
        {
            size_t nParts = 0, nGaussians = 0;

            for (const auto& m_beacon : m_beacons)
            {
                switch (m_beacon.m_typePDF)
                {
                    case CBeacon::pdfMonteCarlo:
                        nParts += m_beacon.m_locationMC.size();
                        break;
                    case CBeacon::pdfSOG:
                        nGaussians += m_beacon.m_locationSOG.size();
                        break;
                    case CBeacon::pdfGauss:
                        nGaussians++;
                        break;
                };
            }

            fprintf(
                f, "%u %u", static_cast<unsigned>(nParts),
                static_cast<unsigned>(nGaussians));
            os::fclose(f);
        }
    }

    MRPT_END
}

/*---------------------------------------------------------------
                        getAs3DObject
  ---------------------------------------------------------------*/
void CBeaconMap::getAs3DObject(mrpt::opengl::CSetOfObjects::Ptr& outObj) const
{
    MRPT_START

    if (!genericMapParams.enableSaveAs3DObject) return;

    // ------------------------------------------------
    //  Add the XYZ corner for the current area:
    // ------------------------------------------------
    outObj->insert(opengl::stock_objects::CornerXYZ());

    // Save 3D ellipsoids or whatever representation:
    for (const auto& m_beacon : m_beacons) m_beacon.getAs3DObject(outObj);

    MRPT_END
}

/*---------------------------------------------------------------
   Computes the ratio in [0,1] of correspondences between "this" and the
 "otherMap" map, whose 6D pose relative to "this" is "otherMapPose"
 *   In the case of a multi-metric map, this returns the average between the
 maps. This method always return 0 for grid maps.
 * \param  otherMap                   [IN] The other map to compute the matching
 with.
 * \param  otherMapPose               [IN] The 6D pose of the other map as seen
 from "this".
 * \param  maxDistForCorr             [IN] The minimum distance between 2
 non-probabilistic map elements for counting them as a correspondence.
 * \param  maxMahaDistForCorr         [IN] The minimum Mahalanobis distance
 between 2 probabilistic map elements for counting them as a correspondence.
 *
 * \return The matching ratio [0,1]
 * \sa computeMatchingWith2D
 ----------------------------------------------------------------*/
float CBeaconMap::compute3DMatchingRatio(
    const mrpt::maps::CMetricMap* otherMap2,
    const mrpt::poses::CPose3D& otherMapPose,
    const TMatchingRatioParams& params) const
{
    MRPT_START

    // Compare to a similar map only:
    const CBeaconMap* otherMap = nullptr;

    if (otherMap2->GetRuntimeClass() == CLASS_ID(CBeaconMap))
        otherMap = dynamic_cast<const CBeaconMap*>(otherMap2);

    if (!otherMap) return 0;

    TMatchingPairList matchList;
    vector<bool> otherCorrespondences;
    float out_corrsRatio;

    CBeaconMap modMap;

    modMap.changeCoordinatesReference(otherMapPose, otherMap);

    computeMatchingWith3DLandmarks(
        &modMap, matchList, out_corrsRatio, otherCorrespondences);

    return out_corrsRatio;

    MRPT_END
}

/*---------------------------------------------------------------
                    getBeaconByID
 ---------------------------------------------------------------*/
const CBeacon* CBeaconMap::getBeaconByID(CBeacon::TBeaconID id) const
{
    for (const auto& m_beacon : m_beacons)
        if (m_beacon.m_ID == id) return &m_beacon;
    return nullptr;
}

/*---------------------------------------------------------------
                    getBeaconByID
 ---------------------------------------------------------------*/
CBeacon* CBeaconMap::getBeaconByID(CBeacon::TBeaconID id)
{
    for (auto& m_beacon : m_beacons)
        if (m_beacon.m_ID == id) return &m_beacon;
    return nullptr;
}

/*---------------------------------------------------------------
                    saveToTextFile
- VX VY VZ: Variances of each dimension (C11, C22, C33)
- DET2D DET3D: Determinant of the 2D and 3D covariance matrixes.
- C12, C13, C23: Cross covariances
 ---------------------------------------------------------------*/
void CBeaconMap::saveToTextFile(const string& fil) const
{
    MRPT_START
    FILE* f = os::fopen(fil.c_str(), "wt");
    ASSERT_(f != nullptr);

    for (const auto& m_beacon : m_beacons)
    {
        const auto [C, p] = m_beacon.getCovarianceAndMean();

        float D3 = C.det();
        float D2 = C(0, 0) * C(1, 1) - square(C(0, 1));
        os::fprintf(
            f, "%i %f %f %f %e %e %e %e %e %e %e %e\n",
            static_cast<int>(m_beacon.m_ID), p.x(), p.y(), p.z(), C(0, 0),
            C(1, 1), C(2, 2), D2, D3, C(0, 1), C(1, 2), C(1, 2));
    }

    os::fclose(f);
    MRPT_END
}