CICP.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 "slam-precomp.h"  // Precompiled headers

#include <mrpt/config/CConfigFileBase.h>  // MRPT_LOAD_*()
#include <mrpt/math/TPose2D.h>
#include <mrpt/math/ops_containers.h>
#include <mrpt/math/wrap2pi.h>
#include <mrpt/poses/CPose2D.h>
#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/poses/CPosePDFGaussian.h>
#include <mrpt/poses/CPosePDFSOG.h>
#include <mrpt/serialization/CArchive.h>
#include <mrpt/slam/CICP.h>
#include <mrpt/system/CTicTac.h>
#include <mrpt/tfest.h>
#include <Eigen/Dense>

using namespace mrpt::slam;
using namespace mrpt::maps;
using namespace mrpt::math;
using namespace mrpt::tfest;
using namespace mrpt::system;
using namespace mrpt::poses;
using namespace std;

CPosePDF::Ptr CICP::AlignPDF(
    const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* mm2,
    const CPosePDFGaussian& initialEstimationPDF,
    mrpt::optional_ref<TMetricMapAlignmentResult> outInfo)
{
    MRPT_START

    CTicTac tictac;
    TReturnInfo outInfoVal;
    CPosePDF::Ptr resultPDF;

    if (outInfo) tictac.Tic();

    switch (options.ICP_algorithm)
    {
        case icpClassic:
            resultPDF =
                ICP_Method_Classic(m1, mm2, initialEstimationPDF, outInfoVal);
            break;
        case icpLevenbergMarquardt:
            resultPDF =
                ICP_Method_LM(m1, mm2, initialEstimationPDF, outInfoVal);
            break;
        default:
            THROW_EXCEPTION_FMT(
                "Invalid value for ICP_algorithm: %i",
                static_cast<int>(options.ICP_algorithm));
    }  // end switch

    if (outInfo) outInfoVal.executionTime = tictac.Tac();

    // Copy the output info if requested:
    if (outInfo)
        if (auto* o = dynamic_cast<TReturnInfo*>(&outInfo.value().get()); o)
            *o = outInfoVal;

    return resultPDF;

    MRPT_END
}

/*---------------------------------------------------------------
                    loadFromConfigFile
  ---------------------------------------------------------------*/
void CICP::TConfigParams::loadFromConfigFile(
    const mrpt::config::CConfigFileBase& iniFile, const std::string& section)
{
    MRPT_LOAD_CONFIG_VAR(maxIterations, int, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(minAbsStep_trans, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(minAbsStep_rot, float, iniFile, section);

    ICP_algorithm = iniFile.read_enum<TICPAlgorithm>(
        section, "ICP_algorithm", ICP_algorithm);
    ICP_covariance_method = iniFile.read_enum<TICPCovarianceMethod>(
        section, "ICP_covariance_method", ICP_covariance_method);

    MRPT_LOAD_CONFIG_VAR(thresholdDist, float, iniFile, section);
    thresholdAng = DEG2RAD(iniFile.read_float(
        section.c_str(), "thresholdAng_DEG", RAD2DEG(thresholdAng)));

    MRPT_LOAD_CONFIG_VAR(ALFA, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(smallestThresholdDist, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(onlyUniqueRobust, bool, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(doRANSAC, bool, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(covariance_varPoints, float, iniFile, section);

    MRPT_LOAD_CONFIG_VAR(ransac_minSetSize, int, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(ransac_maxSetSize, int, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(
        ransac_mahalanobisDistanceThreshold, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(ransac_nSimulations, int, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(normalizationStd, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(ransac_fuseByCorrsMatch, bool, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(ransac_fuseMaxDiffXY, float, iniFile, section);
    ransac_fuseMaxDiffPhi = DEG2RAD(iniFile.read_float(
        section.c_str(), "ransac_fuseMaxDiffPhi_DEG",
        RAD2DEG(ransac_fuseMaxDiffPhi)));

    MRPT_LOAD_CONFIG_VAR(kernel_rho, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(use_kernel, bool, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(Axy_aprox_derivatives, float, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(LM_initial_lambda, float, iniFile, section);

    MRPT_LOAD_CONFIG_VAR(skip_cov_calculation, bool, iniFile, section);
    MRPT_LOAD_CONFIG_VAR(skip_quality_calculation, bool, iniFile, section);

    MRPT_LOAD_CONFIG_VAR(
        corresponding_points_decimation, int, iniFile, section);
}

void CICP::TConfigParams::saveToConfigFile(
    mrpt::config::CConfigFileBase& c, const std::string& s) const
{
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        ICP_algorithm, "The ICP algorithm to use (see enum TICPAlgorithm)");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        ICP_covariance_method,
        "Method to use for covariance estimation (see enum "
        "TICPCovarianceMethod)");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        onlyUniqueRobust,
        "Only the closest correspondence for each reference point will be "
        "kept");
    MRPT_SAVE_CONFIG_VAR_COMMENT(maxIterations, "Iterations");
    MRPT_SAVE_CONFIG_VAR_COMMENT(minAbsStep_trans, "Termination criteria");
    MRPT_SAVE_CONFIG_VAR_COMMENT(minAbsStep_rot, "Termination criteria");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        thresholdDist,
        "Initial threshold distance for two points to be a match");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        thresholdAng,
        "Initial threshold distance for two points to be a match");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        ALFA,
        "The scale factor for thresholds every time convergence is achieved");
    MRPT_SAVE_CONFIG_VAR_COMMENT(
        smallestThresholdDist,
        "The size for threshold such that iterations will stop, "
        "since it is considered precise enough.");
    MRPT_SAVE_CONFIG_VAR_COMMENT(covariance_varPoints, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(doRANSAC, "Perform a RANSAC step");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_minSetSize, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_maxSetSize, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_nSimulations, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_mahalanobisDistanceThreshold, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(normalizationStd, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_fuseByCorrsMatch, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_fuseMaxDiffXY, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(ransac_fuseMaxDiffPhi, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(kernel_rho, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(use_kernel, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(Axy_aprox_derivatives, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(LM_initial_lambda, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(skip_cov_calculation, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(skip_quality_calculation, "");
    MRPT_SAVE_CONFIG_VAR_COMMENT(corresponding_points_decimation, "");
}

float CICP::kernel(float x2, float rho2)
{
    return options.use_kernel ? (x2 / (x2 + rho2)) : x2;
}

/*----------------------------------------------------------------------------

                    ICP_Method_Classic

  ----------------------------------------------------------------------------*/
CPosePDF::Ptr CICP::ICP_Method_Classic(
    const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* mm2,
    const CPosePDFGaussian& initialEstimationPDF, TReturnInfo& outInfo)
{
    MRPT_START

    // The result can be either a Gaussian or a SOG:
    CPosePDF::Ptr resultPDF;
    CPosePDFGaussian::Ptr gaussPdf;
    CPosePDFSOG::Ptr SOG;

    size_t nCorrespondences = 0;
    bool keepApproaching;
    CPose2D grossEst = initialEstimationPDF.mean;
    mrpt::tfest::TMatchingPairList correspondences, old_correspondences;
    CPose2D lastMeanPose;

    // Assure the class of the maps:
    const mrpt::maps::CMetricMap* m2 = mm2;

    // Asserts:
    // -----------------
    ASSERT_(options.ALFA >= 0 && options.ALFA < 1);

    // The algorithm output auxiliar info:
    // -------------------------------------------------
    outInfo.nIterations = 0;
    outInfo.goodness = 1;
    outInfo.quality = 0;

    // The gaussian PDF to estimate:
    // ------------------------------------------------------
    gaussPdf = std::make_shared<CPosePDFGaussian>();

    // First gross approximation:
    gaussPdf->mean = grossEst;

    // Initial thresholds:
    mrpt::maps::TMatchingParams matchParams;
    mrpt::maps::TMatchingExtraResults matchExtraResults;

    matchParams.maxDistForCorrespondence =
        options.thresholdDist;  // Distance threshold.
    matchParams.maxAngularDistForCorrespondence =
        options.thresholdAng;  // Angular threshold.
    // Option onlyClosestCorrespondences removed in MRPT 2.0.
    matchParams.onlyKeepTheClosest = true;
    matchParams.onlyUniqueRobust = options.onlyUniqueRobust;
    matchParams.decimation_other_map_points =
        options.corresponding_points_decimation;

    // Ensure maps are not empty!
    // ------------------------------------------------------
    if (!m2->isEmpty())
    {
        matchParams.offset_other_map_points = 0;

        // ------------------------------------------------------
        //                  The ICP loop
        // ------------------------------------------------------
        do
        {
            // ------------------------------------------------------
            //      Find the matching (for a points map)
            // ------------------------------------------------------
            matchParams.angularDistPivotPoint = TPoint3D(
                gaussPdf->mean.x(), gaussPdf->mean.y(),
                0);  // Pivot point for angular measurements

            m1->determineMatching2D(
                m2,  // The other map
                gaussPdf->mean,  // The other map pose
                correspondences, matchParams, matchExtraResults);

            nCorrespondences = correspondences.size();

            // ***DEBUG***
            //              correspondences.dumpToFile("debug_correspondences.txt");

            if (!nCorrespondences)
            {
                // Nothing we can do !!
                keepApproaching = false;
            }
            else
            {
                // Compute the estimated pose.
                //  (Method from paper of J.Gonzalez, Martinez y Morales)
                // ----------------------------------------------------------------------
                mrpt::math::TPose2D est_mean;
                mrpt::tfest::se2_l2(correspondences, est_mean);

                gaussPdf->mean = mrpt::poses::CPose2D(est_mean);

                // If matching has not changed, decrease the thresholds:
                // --------------------------------------------------------
                keepApproaching = true;
                if (!(fabs(lastMeanPose.x() - gaussPdf->mean.x()) >
                          options.minAbsStep_trans ||
                      fabs(lastMeanPose.y() - gaussPdf->mean.y()) >
                          options.minAbsStep_trans ||
                      fabs(math::wrapToPi(
                          lastMeanPose.phi() - gaussPdf->mean.phi())) >
                          options.minAbsStep_rot))
                {
                    matchParams.maxDistForCorrespondence *= options.ALFA;
                    matchParams.maxAngularDistForCorrespondence *= options.ALFA;
                    if (matchParams.maxDistForCorrespondence <
                        options.smallestThresholdDist)
                        keepApproaching = false;

                    if (++matchParams.offset_other_map_points >=
                        options.corresponding_points_decimation)
                        matchParams.offset_other_map_points = 0;
                }

                lastMeanPose = gaussPdf->mean;

            }  // end of "else, there are correspondences"

            // Next iteration:
            outInfo.nIterations++;

            if (outInfo.nIterations >= options.maxIterations &&
                matchParams.maxDistForCorrespondence >
                    options.smallestThresholdDist)
            {
                matchParams.maxDistForCorrespondence *= options.ALFA;
            }

        } while (
            (keepApproaching && outInfo.nIterations < options.maxIterations) ||
            (outInfo.nIterations >= options.maxIterations &&
             matchParams.maxDistForCorrespondence >
                 options.smallestThresholdDist));

        // -------------------------------------------------
        //   Obtain the covariance matrix of the estimation
        // -------------------------------------------------
        if (!options.skip_cov_calculation && nCorrespondences)
        {
            switch (options.ICP_covariance_method)
            {
                // ----------------------------------------------
                // METHOD: MSE linear estimation
                // ----------------------------------------------
                case icpCovLinealMSE:
                    mrpt::tfest::se2_l2(correspondences, *gaussPdf);
                    // Scale covariance:
                    gaussPdf->cov *= options.covariance_varPoints;
                    break;

                // ----------------------------------------------
                // METHOD: Method from Oxford MRG's "OXSMV2"
                //
                //  It is the equivalent covariance resulting
                //   from a Levenberg-Maquardt optimization stage.
                // ----------------------------------------------
                case icpCovFiniteDifferences:
                {
                    CMatrixDouble D(3, nCorrespondences);

                    const TPose2D transf = gaussPdf->mean.asTPose();

                    double ccos = cos(transf.phi);
                    double csin = sin(transf.phi);

                    double w1, w2, w3;
                    double q1, q2, q3;
                    double A, B;
                    double Axy = 0.01;

                    // Fill out D:
                    double rho2 = square(options.kernel_rho);
                    mrpt::tfest::TMatchingPairList::iterator it;
                    size_t i;
                    for (i = 0, it = correspondences.begin();
                         i < nCorrespondences; ++i, ++it)
                    {
                        float other_x_trans =
                            transf.x + ccos * it->other_x - csin * it->other_y;
                        float other_y_trans =
                            transf.y + csin * it->other_x + ccos * it->other_y;

                        // Jacobian: dR2_dx
                        // --------------------------------------
                        w1 = other_x_trans - Axy;
                        q1 = kernel(
                            square(it->this_x - w1) +
                                square(it->this_y - other_y_trans),
                            rho2);

                        w2 = other_x_trans;
                        q2 = kernel(
                            square(it->this_x - w2) +
                                square(it->this_y - other_y_trans),
                            rho2);

                        w3 = other_x_trans + Axy;
                        q3 = kernel(
                            square(it->this_x - w3) +
                                square(it->this_y - other_y_trans),
                            rho2);

                        // interpolate
                        A = ((q3 - q2) / ((w3 - w2) * (w3 - w1))) -
                            ((q1 - q2) / ((w1 - w2) * (w3 - w1)));
                        B = ((q1 - q2) + (A * ((w2 * w2) - (w1 * w1)))) /
                            (w1 - w2);

                        D(0, i) = (2 * A * other_x_trans) + B;

                        // Jacobian: dR2_dy
                        // --------------------------------------
                        w1 = other_y_trans - Axy;
                        q1 = kernel(
                            square(it->this_x - other_x_trans) +
                                square(it->this_y - w1),
                            rho2);

                        w2 = other_y_trans;
                        q2 = kernel(
                            square(it->this_x - other_x_trans) +
                                square(it->this_y - w2),
                            rho2);

                        w3 = other_y_trans + Axy;
                        q3 = kernel(
                            square(it->this_x - other_x_trans) +
                                square(it->this_y - w3),
                            rho2);

                        // interpolate
                        A = ((q3 - q2) / ((w3 - w2) * (w3 - w1))) -
                            ((q1 - q2) / ((w1 - w2) * (w3 - w1)));
                        B = ((q1 - q2) + (A * ((w2 * w2) - (w1 * w1)))) /
                            (w1 - w2);

                        D(1, i) = (2 * A * other_y_trans) + B;

                        // Jacobian: dR_dphi
                        // --------------------------------------
                        D(2, i) =
                            D(0, i) *
                                (-csin * it->other_x - ccos * it->other_y) +
                            D(1, i) * (ccos * it->other_x - csin * it->other_y);

                    }  // end for each corresp.

                    // COV = ( D*D^T + lamba*I )^-1
                    CMatrixDouble33 DDt;
                    DDt.matProductOf_AAt(D);

                    for (i = 0; i < 3; i++)
                        DDt(i, i) += 1e-6;  // Just to make sure the matrix is
                    // not singular, while not changing
                    // its covariance significantly.

                    gaussPdf->cov = DDt.inverse_LLt();
                }
                break;
                default:
                    THROW_EXCEPTION_FMT(
                        "Invalid value for ICP_covariance_method: %i",
                        static_cast<int>(options.ICP_covariance_method));
            }
        }

        outInfo.goodness = matchExtraResults.correspondencesRatio;

        if (!nCorrespondences || options.skip_quality_calculation)
        {
            outInfo.quality = matchExtraResults.correspondencesRatio;
        }
        else
        {
            // Compute a crude estimate of the quality of scan matching at this
            // local minimum:
            // -----------------------------------------------------------------------------------
            float Axy = matchParams.maxDistForCorrespondence * 0.9f;

            CPose2D P0(gaussPdf->mean);
            CPose2D PX1(P0);
            PX1.x_incr(-Axy);
            CPose2D PX2(P0);
            PX2.x_incr(+Axy);
            CPose2D PY1(P0);
            PY1.y_incr(-Axy);
            CPose2D PY2(P0);
            PY2.y_incr(+Axy);

            matchParams.angularDistPivotPoint =
                TPoint3D(gaussPdf->mean.x(), gaussPdf->mean.y(), 0);
            m1->determineMatching2D(
                m2,  // The other map
                P0,  // The other map pose
                correspondences, matchParams, matchExtraResults);
            const float E0 = matchExtraResults.correspondencesRatio;

            m1->determineMatching2D(
                m2,  // The other map
                PX1,  // The other map pose
                correspondences, matchParams, matchExtraResults);
            const float EX1 = matchExtraResults.correspondencesRatio;

            m1->determineMatching2D(
                m2,  // The other map
                PX2,  // The other map pose
                correspondences, matchParams, matchExtraResults);
            const float EX2 = matchExtraResults.correspondencesRatio;

            m1->determineMatching2D(
                m2,  // The other map
                PY1,  // The other map pose
                correspondences, matchParams, matchExtraResults);
            const float EY1 = matchExtraResults.correspondencesRatio;
            m1->determineMatching2D(
                m2,  // The other map
                PY2,  // The other map pose
                correspondences, matchParams, matchExtraResults);
            const float EY2 = matchExtraResults.correspondencesRatio;

            outInfo.quality =
                -max(EX1 - E0, max(EX2 - E0, max(EY1 - E0, EY2 - E0))) /
                (E0 + 1e-1);
        }

    }  // end of "if m2 is not empty"

    // We'll return a CPosePDFGaussian or a CPosePDFSOG if RANSAC is enabled:
    // -----------------------------------------------------------------------

    // RANSAC?
    if (options.doRANSAC)
    {
        mrpt::tfest::TSE2RobustParams params;
        params.ransac_minSetSize = options.ransac_minSetSize;
        params.ransac_maxSetSize = options.ransac_maxSetSize;
        params.ransac_mahalanobisDistanceThreshold =
            options.ransac_mahalanobisDistanceThreshold;
        params.ransac_nSimulations = options.ransac_nSimulations;
        params.ransac_fuseByCorrsMatch = options.ransac_fuseByCorrsMatch;
        params.ransac_fuseMaxDiffXY = options.ransac_fuseMaxDiffXY;
        params.ransac_fuseMaxDiffPhi = options.ransac_fuseMaxDiffPhi;
        params.ransac_algorithmForLandmarks = false;

        mrpt::tfest::TSE2RobustResult results;
        mrpt::tfest::se2_l2_robust(
            correspondences, options.normalizationStd, params, results);

        SOG = std::make_shared<CPosePDFSOG>();
        *SOG = results.transformation;

        // And return the SOG:
        resultPDF = SOG;
    }
    else
    {
        // Return the gaussian distribution:
        resultPDF = gaussPdf;
    }

    return resultPDF;

    MRPT_END
}

/*----------------------------------------------------------------------------

                        ICP_Method_LM

  ----------------------------------------------------------------------------*/
CPosePDF::Ptr CICP::ICP_Method_LM(
    const mrpt::maps::CMetricMap* mm1, const mrpt::maps::CMetricMap* m2,
    const CPosePDFGaussian& initialEstimationPDF, TReturnInfo& outInfo)
{
    MRPT_START

    // The result can be either a Gaussian or a SOG:
    size_t nCorrespondences = 0;
    bool keepIteratingICP;
    CPose2D grossEst = initialEstimationPDF.mean;
    TMatchingPairList correspondences, old_correspondences;
    CPose2D lastMeanPose;
    std::vector<float> other_xs_trans, other_ys_trans;  // temporary container
    // of "other" map (map2)
    // transformed by "q"
    CMatrixFloat dJ_dq;  // The jacobian
    CPose2D q;  // The updated 2D transformation estimate
    CPose2D q_new;

    const bool onlyUniqueRobust = options.onlyUniqueRobust;

    // Assure the class of the maps:
    ASSERT_(mm1->GetRuntimeClass()->derivedFrom(CLASS_ID(CPointsMap)));
    const auto* m1 = dynamic_cast<const CPointsMap*>(mm1);

    // Asserts:
    // -----------------
    ASSERT_(options.ALFA > 0 && options.ALFA < 1);

    // The algorithm output auxiliar info:
    // -------------------------------------------------
    outInfo.nIterations = 0;
    outInfo.goodness = 1.0f;

    TMatchingParams matchParams;
    TMatchingExtraResults matchExtraResults;

    matchParams.maxDistForCorrespondence =
        options.thresholdDist;  // Distance threshold
    matchParams.maxAngularDistForCorrespondence =
        options.thresholdAng;  // Angular threshold
    matchParams.angularDistPivotPoint =
        TPoint3D(q.x(), q.y(), 0);  // Pivot point for angular measurements
    matchParams.onlyKeepTheClosest = true;
    matchParams.onlyUniqueRobust = onlyUniqueRobust;
    matchParams.decimation_other_map_points =
        options.corresponding_points_decimation;

    // The gaussian PDF to estimate:
    // ------------------------------------------------------
    // First gross approximation:
    q = grossEst;

    // For LM inverse
    CMatrixFixed<float, 3, 3> C;
    CMatrixFixed<float, 3, 3>
        C_inv;  // This will keep the cov. matrix at the end

    // Ensure maps are not empty!
    // ------------------------------------------------------
    if (!m2->isEmpty())
    {
        matchParams.offset_other_map_points = 0;
        // ------------------------------------------------------
        //                  The ICP loop
        // ------------------------------------------------------
        do
        {
            // ------------------------------------------------------
            //      Find the matching (for a points map)
            // ------------------------------------------------------
            m1->determineMatching2D(
                m2,  // The other map
                q,  // The other map pose
                correspondences, matchParams, matchExtraResults);

            nCorrespondences = correspondences.size();

            if (!nCorrespondences)
            {
                // Nothing we can do !!
                keepIteratingICP = false;
            }
            else
            {
                // Compute the estimated pose through iterative least-squares:
                // Levenberg-Marquardt
                // ----------------------------------------------------------------------
                dJ_dq.setSize(3, nCorrespondences);  // The jacobian of the
                // error function wrt the
                // transformation q

                double lambda = options.LM_initial_lambda;  // The LM parameter

                double ccos = cos(q.phi());
                double csin = sin(q.phi());

                double w1, w2, w3;
                double q1, q2, q3;
                double A, B;
                const double Axy =
                    options.Axy_aprox_derivatives;  // For approximating the
                // derivatives

                // Compute at once the square errors for each point with the
                // current "q" and the transformed points:
                std::vector<float> sq_errors;
                correspondences.squareErrorVector(
                    q, sq_errors, other_xs_trans, other_ys_trans);
                double OSE_initial = math::sum(sq_errors);

                // Compute "dJ_dq"
                // ------------------------------------
                double rho2 = square(options.kernel_rho);
                mrpt::tfest::TMatchingPairList::iterator it;
                std::vector<float>::const_iterator other_x_trans, other_y_trans;
                size_t i;

                for (i = 0, it = correspondences.begin(),
                    other_x_trans = other_xs_trans.begin(),
                    other_y_trans = other_ys_trans.begin();
                     i < nCorrespondences;
                     ++i, ++it, ++other_x_trans, ++other_y_trans)
                {
                    // Jacobian: dJ_dx
                    // --------------------------------------
                    //#define ICP_DISTANCES_TO_LINE

#ifndef ICP_DISTANCES_TO_LINE
                    w1 = *other_x_trans - Axy;
                    q1 = m1->squareDistanceToClosestCorrespondence(
                        w1, *other_y_trans);
                    q1 = kernel(q1, rho2);

                    w2 = *other_x_trans;
                    q2 = m1->squareDistanceToClosestCorrespondence(
                        w2, *other_y_trans);
                    q2 = kernel(q2, rho2);

                    w3 = *other_x_trans + Axy;
                    q3 = m1->squareDistanceToClosestCorrespondence(
                        w3, *other_y_trans);
                    q3 = kernel(q3, rho2);
#else
                    // The distance to the line that interpolates the TWO
                    // closest points:
                    float x1, y1, x2, y2, d1, d2;
                    m1->kdTreeTwoClosestPoint2D(
                        *other_x_trans, *other_y_trans,  // The query
                        x1, y1,  // Closest point #1
                        x2, y2,  // Closest point #2
                        d1, d2);

                    w1 = *other_x_trans - Axy;
                    q1 = math::closestSquareDistanceFromPointToLine(
                        w1, *other_y_trans, x1, y1, x2, y2);
                    q1 = kernel(q1, rho2);

                    w2 = *other_x_trans;
                    q2 = math::closestSquareDistanceFromPointToLine(
                        w2, *other_y_trans, x1, y1, x2, y2);
                    q2 = kernel(q2, rho2);

                    w3 = *other_x_trans + Axy;
                    q3 = math::closestSquareDistanceFromPointToLine(
                        w3, *other_y_trans, x1, y1, x2, y2);
                    q3 = kernel(q3, rho2);
#endif
                    // interpolate
                    A = ((q3 - q2) / ((w3 - w2) * (w3 - w1))) -
                        ((q1 - q2) / ((w1 - w2) * (w3 - w1)));
                    B = ((q1 - q2) + (A * ((w2 * w2) - (w1 * w1)))) / (w1 - w2);

                    dJ_dq(0, i) = (2 * A * *other_x_trans) + B;

                    // Jacobian: dJ_dy
                    // --------------------------------------
                    w1 = *other_y_trans - Axy;
#ifdef ICP_DISTANCES_TO_LINE
                    q1 = math::closestSquareDistanceFromPointToLine(
                        *other_x_trans, w1, x1, y1, x2, y2);
                    q1 = kernel(q1, rho2);
#else
                    q1 = kernel(
                        square(it->this_x - *other_x_trans) +
                            square(it->this_y - w1),
                        rho2);
#endif

                    w2 = *other_y_trans;
                    // q2 is alreay computed from above!
                    // q2 = m1->squareDistanceToClosestCorrespondence(
                    // *other_x_trans, w2 );
                    // q2= kernel( square(it->this_x - *other_x_trans)+ square(
                    // it->this_y - w2 ),  rho2 );

                    w3 = *other_y_trans + Axy;
#ifdef ICP_DISTANCES_TO_LINE
                    q3 = math::closestSquareDistanceFromPointToLine(
                        *other_x_trans, w3, x1, y1, x2, y2);
                    q3 = kernel(q3, rho2);
#else
                    q3 = kernel(
                        square(it->this_x - *other_x_trans) +
                            square(it->this_y - w3),
                        rho2);
#endif

                    // interpolate
                    A = ((q3 - q2) / ((w3 - w2) * (w3 - w1))) -
                        ((q1 - q2) / ((w1 - w2) * (w3 - w1)));
                    B = ((q1 - q2) + (A * ((w2 * w2) - (w1 * w1)))) / (w1 - w2);

                    dJ_dq(1, i) = (2 * A * *other_y_trans) + B;

                    // Jacobian: dR_dphi
                    // --------------------------------------
                    dJ_dq(2, i) =
                        dJ_dq(0, i) *
                            (-csin * it->other_x - ccos * it->other_y) +
                        dJ_dq(1, i) * (ccos * it->other_x - csin * it->other_y);

                }  // end for each corresp.

                // Now we have the Jacobian in dJ_dq.

                // Compute the Hessian matrix H = dJ_dq * dJ_dq^T
                CMatrixFloat H_(3, 3);
                H_.matProductOf_AAt(dJ_dq);

                CMatrixFixed<float, 3, 3> H;
                H = H_;

                bool keepIteratingLM = true;

                // ---------------------------------------------------
                // Iterate the inner LM loop until convergence:
                // ---------------------------------------------------
                q_new = q;

                std::vector<float> new_sq_errors, new_other_xs_trans,
                    new_other_ys_trans;
                size_t nLMiters = 0;
                const size_t maxLMiters = 100;

                while (keepIteratingLM && ++nLMiters < maxLMiters)
                {
                    // The LM heuristic is:
                    //  x_{k+1} = x_k  - ( H + \lambda diag(H) )^-1 * grad(J)
                    //  grad(J) = dJ_dq * e (vector of errors)
                    C = H;
                    for (i = 0; i < 3; i++)
                        C(i, i) *=
                            (1 + lambda);  // Levenberg-Maquardt heuristic

                    C_inv = C.inverse_LLt();

                    // LM_delta = C_inv * dJ_dq * sq_errors
                    const Eigen::Vector3f LM_delta =
                        (C_inv.asEigen() * dJ_dq.asEigen() *
                         Eigen::Map<Eigen::VectorXf>(
                             &sq_errors[0], sq_errors.size()))
                            .eval();

                    q_new.x(q.x() - LM_delta[0]);
                    q_new.y(q.y() - LM_delta[1]);
                    q_new.phi(q.phi() - LM_delta[2]);

                    // Compute the new square errors:
                    // ---------------------------------------
                    correspondences.squareErrorVector(
                        q_new, new_sq_errors, new_other_xs_trans,
                        new_other_ys_trans);

                    float OSE_new = math::sum(new_sq_errors);

                    bool improved = OSE_new < OSE_initial;

#if 0  // Debuggin'
                    cout << "_____________" << endl;
                    cout << "q -> q_new   : " << q << " -> " << q_new << endl;
                    printf("err: %f  -> %f    lambda: %e\n", OSE_initial ,OSE_new, lambda );
                    cout << "\\/J = "; utils::operator <<(cout,dJsq); cout << endl;
                    mrpt::system::pause();
#endif

                    keepIteratingLM =
                        fabs(LM_delta[0]) > options.minAbsStep_trans ||
                        fabs(LM_delta[1]) > options.minAbsStep_trans ||
                        fabs(LM_delta[2]) > options.minAbsStep_rot;

                    if (improved)
                    {
                        // If resids have gone down, keep change and make lambda
                        // smaller by factor of 10
                        lambda /= 10;
                        q = q_new;
                        OSE_initial = OSE_new;
                    }
                    else
                    {
                        // Discard movement and try with larger lambda:
                        lambda *= 10;
                    }

                }  // end iterative LM

#if 0  // Debuggin'
                cout << "ICP loop: " << lastMeanPose  << " -> " << q << " LM iters: " << nLMiters  << " threshold: " << matchParams.maxDistForCorrespondence << endl;
#endif
                // --------------------------------------------------------
                // now the conditions for the outer ICP loop
                // --------------------------------------------------------
                keepIteratingICP = true;
                if (fabs(lastMeanPose.x() - q.x()) < options.minAbsStep_trans &&
                    fabs(lastMeanPose.y() - q.y()) < options.minAbsStep_trans &&
                    fabs(math::wrapToPi(lastMeanPose.phi() - q.phi())) <
                        options.minAbsStep_rot)
                {
                    matchParams.maxDistForCorrespondence *= options.ALFA;
                    matchParams.maxAngularDistForCorrespondence *= options.ALFA;
                    if (matchParams.maxDistForCorrespondence <
                        options.smallestThresholdDist)
                        keepIteratingICP = false;

                    if (++matchParams.offset_other_map_points >=
                        options.corresponding_points_decimation)
                        matchParams.offset_other_map_points = 0;
                }
                lastMeanPose = q;
            }  // end of "else, there are correspondences"

            // Next iteration:
            outInfo.nIterations++;

            if (outInfo.nIterations >= options.maxIterations &&
                matchParams.maxDistForCorrespondence >
                    options.smallestThresholdDist)
            {
                matchParams.maxDistForCorrespondence *= options.ALFA;
            }

        } while (
            (keepIteratingICP && outInfo.nIterations < options.maxIterations) ||
            (outInfo.nIterations >= options.maxIterations &&
             matchParams.maxDistForCorrespondence >
                 options.smallestThresholdDist));

        outInfo.goodness = matchExtraResults.correspondencesRatio;

        // if (!options.skip_quality_calculation)
        {
            outInfo.quality = matchExtraResults.correspondencesRatio;
        }

    }  // end of "if m2 is not empty"

    return CPosePDFGaussian::Create(q, C_inv.cast_double());
    MRPT_END
}

CPose3DPDF::Ptr CICP::Align3DPDF(
    const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* mm2,
    const CPose3DPDFGaussian& initialEstimationPDF,
    mrpt::optional_ref<TMetricMapAlignmentResult> outInfo)
{
    MRPT_START

    static CTicTac tictac;
    TReturnInfo outInfoVal;
    CPose3DPDF::Ptr resultPDF;

    if (outInfo) tictac.Tic();

    switch (options.ICP_algorithm)
    {
        case icpClassic:
            resultPDF =
                ICP3D_Method_Classic(m1, mm2, initialEstimationPDF, outInfoVal);
            break;
        case icpLevenbergMarquardt:
            THROW_EXCEPTION("Only icpClassic is implemented for ICP-3D");
            break;
        default:
            THROW_EXCEPTION_FMT(
                "Invalid value for ICP_algorithm: %i",
                static_cast<int>(options.ICP_algorithm));
    }  // end switch

    if (outInfo) outInfoVal.executionTime = tictac.Tac();

    // Copy the output info if requested:
    if (outInfo)
        if (auto* o = dynamic_cast<TReturnInfo*>(&outInfo.value().get()); o)
            *o = outInfoVal;

    return resultPDF;

    MRPT_END
}

CPose3DPDF::Ptr CICP::ICP3D_Method_Classic(
    const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* mm2,
    const CPose3DPDFGaussian& initialEstimationPDF, TReturnInfo& outInfo)
{
    MRPT_START

    // The result can be either a Gaussian or a SOG:
    CPose3DPDF::Ptr resultPDF;
    CPose3DPDFGaussian::Ptr gaussPdf;

    size_t nCorrespondences = 0;
    bool keepApproaching;
    CPose3D grossEst = initialEstimationPDF.mean;
    mrpt::tfest::TMatchingPairList correspondences, old_correspondences;
    CPose3D lastMeanPose;

    // Assure the class of the maps:
    ASSERT_(mm2->GetRuntimeClass()->derivedFrom(CLASS_ID(CPointsMap)));
    const CPointsMap* m2 = (CPointsMap*)mm2;

    // Asserts:
    // -----------------
    ASSERT_(options.ALFA > 0 && options.ALFA < 1);

    // The algorithm output auxiliar info:
    // -------------------------------------------------
    outInfo.nIterations = 0;
    outInfo.goodness = 1;
    outInfo.quality = 0;

    // The gaussian PDF to estimate:
    // ------------------------------------------------------
    gaussPdf = std::make_shared<CPose3DPDFGaussian>();

    // First gross approximation:
    gaussPdf->mean = grossEst;

    // Initial thresholds:
    TMatchingParams matchParams;
    TMatchingExtraResults matchExtraResults;

    matchParams.maxDistForCorrespondence =
        options.thresholdDist;  // Distance threshold
    matchParams.maxAngularDistForCorrespondence =
        options.thresholdAng;  // Angular threshold
    matchParams.onlyKeepTheClosest = true;
    matchParams.onlyUniqueRobust = options.onlyUniqueRobust;
    matchParams.decimation_other_map_points =
        options.corresponding_points_decimation;

    // Ensure maps are not empty!
    // ------------------------------------------------------
    if (!m2->isEmpty())
    {
        matchParams.offset_other_map_points = 0;

        // ------------------------------------------------------
        //                  The ICP loop
        // ------------------------------------------------------
        do
        {
            matchParams.angularDistPivotPoint = TPoint3D(
                gaussPdf->mean.x(), gaussPdf->mean.y(), gaussPdf->mean.z());

            // ------------------------------------------------------
            //      Find the matching (for a points map)
            // ------------------------------------------------------
            m1->determineMatching3D(
                m2,  // The other map
                gaussPdf->mean,  // The other map pose
                correspondences, matchParams, matchExtraResults);

            nCorrespondences = correspondences.size();

            if (!nCorrespondences)
            {
                // Nothing we can do !!
                keepApproaching = false;
            }
            else
            {
                // Compute the estimated pose, using Horn's method.
                // ----------------------------------------------------------------------
                mrpt::poses::CPose3DQuat estPoseQuat;
                double transf_scale;
                mrpt::tfest::se3_l2(
                    correspondences, estPoseQuat, transf_scale,
                    false /* dont force unit scale */);
                gaussPdf->mean = mrpt::poses::CPose3D(estPoseQuat);

                // If matching has not changed, decrease the thresholds:
                // --------------------------------------------------------
                keepApproaching = true;
                if (!(fabs(lastMeanPose.x() - gaussPdf->mean.x()) >
                          options.minAbsStep_trans ||
                      fabs(lastMeanPose.y() - gaussPdf->mean.y()) >
                          options.minAbsStep_trans ||
                      fabs(lastMeanPose.z() - gaussPdf->mean.z()) >
                          options.minAbsStep_trans ||
                      fabs(math::wrapToPi(
                          lastMeanPose.yaw() - gaussPdf->mean.yaw())) >
                          options.minAbsStep_rot ||
                      fabs(math::wrapToPi(
                          lastMeanPose.pitch() - gaussPdf->mean.pitch())) >
                          options.minAbsStep_rot ||
                      fabs(math::wrapToPi(
                          lastMeanPose.roll() - gaussPdf->mean.roll())) >
                          options.minAbsStep_rot))
                {
                    matchParams.maxDistForCorrespondence *= options.ALFA;
                    matchParams.maxAngularDistForCorrespondence *= options.ALFA;
                    if (matchParams.maxDistForCorrespondence <
                        options.smallestThresholdDist)
                        keepApproaching = false;

                    if (++matchParams.offset_other_map_points >=
                        options.corresponding_points_decimation)
                        matchParams.offset_other_map_points = 0;
                }

                lastMeanPose = gaussPdf->mean;

            }  // end of "else, there are correspondences"

            // Next iteration:
            outInfo.nIterations++;

            if (outInfo.nIterations >= options.maxIterations &&
                matchParams.maxDistForCorrespondence >
                    options.smallestThresholdDist)
            {
                matchParams.maxDistForCorrespondence *= options.ALFA;
            }

        } while (
            (keepApproaching && outInfo.nIterations < options.maxIterations) ||
            (outInfo.nIterations >= options.maxIterations &&
             matchParams.maxDistForCorrespondence >
                 options.smallestThresholdDist));

        // -------------------------------------------------
        //   Obtain the covariance matrix of the estimation
        // -------------------------------------------------
        if (!options.skip_cov_calculation && nCorrespondences)
        {
            // ...
        }

        //
        outInfo.goodness = matchExtraResults.correspondencesRatio;

    }  // end of "if m2 is not empty"

    // Return the gaussian distribution:
    resultPDF = gaussPdf;

    return resultPDF;

    MRPT_END
}