CRangeBearingKFSLAM2D.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/math/TPose2D.h>
#include <mrpt/math/utils.h>
#include <mrpt/math/wrap2pi.h>
#include <mrpt/obs/CActionRobotMovement3D.h>
#include <mrpt/opengl/CEllipsoid2D.h>
#include <mrpt/opengl/CEllipsoid3D.h>
#include <mrpt/opengl/CSetOfObjects.h>
#include <mrpt/opengl/stock_objects.h>
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/poses/CPosePDFGaussian.h>
#include <mrpt/slam/CRangeBearingKFSLAM2D.h>
#include <mrpt/slam/data_association.h>
#include <mrpt/system/CTicTac.h>
#include <mrpt/system/os.h>

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

#define STATS_EXPERIMENT 0
#define STATS_EXPERIMENT_ALSO_NC 1

/*---------------------------------------------------------------
                            Constructor
  ---------------------------------------------------------------*/
CRangeBearingKFSLAM2D::CRangeBearingKFSLAM2D()
    : options(), m_action(), m_SF(), m_IDs(), m_SFs()
{
    reset();
}

/*---------------------------------------------------------------
                            reset
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::reset()
{
    m_action.reset();
    m_SF.reset();
    m_IDs.clear();
    m_SFs.clear();

    // INIT KF STATE
    m_xkk.assign(3, 0);  // State: 3D pose (x,y,phi)

    // Initial cov:
    m_pkk.setSize(3, 3);
    m_pkk.setZero();
}

/*---------------------------------------------------------------
                            Destructor
  ---------------------------------------------------------------*/
CRangeBearingKFSLAM2D::~CRangeBearingKFSLAM2D() = default;
/*---------------------------------------------------------------
                            getCurrentRobotPose
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::getCurrentRobotPose(
    CPosePDFGaussian& out_robotPose) const
{
    MRPT_START

    ASSERT_(m_xkk.size() >= 3);

    // Set 6D pose mean:
    out_robotPose.mean = CPose2D(m_xkk[0], m_xkk[1], m_xkk[2]);

    // and cov:
    out_robotPose.cov = m_pkk.blockCopy<3, 3>(0, 0);

    MRPT_END
}

/*---------------------------------------------------------------
                            getCurrentState
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::getCurrentState(
    CPosePDFGaussian& out_robotPose,
    std::vector<TPoint2D>& out_landmarksPositions,
    std::map<unsigned int, CLandmark::TLandmarkID>& out_landmarkIDs,
    CVectorDouble& out_fullState, CMatrixDouble& out_fullCovariance) const
{
    MRPT_START

    ASSERT_(m_xkk.size() >= 3);

    // Set 6D pose mean:
    out_robotPose.mean = CPose2D(m_xkk[0], m_xkk[1], m_xkk[2]);

    // and cov:
    out_robotPose.cov = m_pkk.blockCopy<3, 3>(0, 0);

    // Landmarks:
    ASSERT_(((m_xkk.size() - 3) % 2) == 0);
    size_t i, nLMs = (m_xkk.size() - 3) / 2;
    out_landmarksPositions.resize(nLMs);
    for (i = 0; i < nLMs; i++)
    {
        out_landmarksPositions[i].x = m_xkk[3 + i * 2 + 0];
        out_landmarksPositions[i].y = m_xkk[3 + i * 2 + 1];
    }  // end for i

    // IDs:
    out_landmarkIDs = m_IDs.getInverseMap();  // m_IDs_inverse;

    // Full state:
    out_fullState.resize(m_xkk.size());
    std::copy(m_xkk.begin(), m_xkk.end(), out_fullState.begin());

    // Full cov:
    out_fullCovariance = m_pkk;

    MRPT_END
}

/*---------------------------------------------------------------
                        processActionObservation
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::processActionObservation(
    CActionCollection::Ptr& action, CSensoryFrame::Ptr& SF)
{
    MRPT_START

    m_action = action;
    m_SF = SF;

    // Sanity check:
    ASSERT_(m_IDs.size() == this->getNumberOfLandmarksInTheMap());

    // ===================================================================================================================
    // Here's the meat!: Call the main method for the KF algorithm, which will
    // call all the callback methods as required:
    // ===================================================================================================================
    runOneKalmanIteration();

    // =============================================================
    //  ADD TO SFs SEQUENCE
    // =============================================================
    if (options.create_simplemap)
    {
        CPosePDFGaussian::Ptr auxPosePDF = std::make_shared<CPosePDFGaussian>();
        getCurrentRobotPose(*auxPosePDF);
        m_SFs.insert(auxPosePDF, SF);
    }

    MRPT_END
}

/** Must return the action vector u.
 * \param out_u The action vector which will be passed to OnTransitionModel
 */
void CRangeBearingKFSLAM2D::OnGetAction(KFArray_ACT& u) const
{
    // Get odometry estimation:
    CActionRobotMovement2D::Ptr actMov2D =
        m_action->getBestMovementEstimation();
    CActionRobotMovement3D::Ptr actMov3D =
        m_action->getActionByClass<CActionRobotMovement3D>();
    if (actMov3D)
    {
        u[0] = actMov3D->poseChange.mean.x();
        u[1] = actMov3D->poseChange.mean.y();
        u[2] = actMov3D->poseChange.mean.yaw();
    }
    else if (actMov2D)
    {
        CPose2D estMovMean;
        actMov2D->poseChange->getMean(estMovMean);
        u[0] = estMovMean.x();
        u[1] = estMovMean.y();
        u[2] = estMovMean.phi();
    }
    else
    {
        // Left u as zeros
        for (size_t i = 0; i < 3; i++) u[i] = 0;
    }
}

/** This virtual function musts implement the prediction model of the Kalman
 * filter.
 */
void CRangeBearingKFSLAM2D::OnTransitionModel(
    const KFArray_ACT& u, KFArray_VEH& xv, bool& out_skipPrediction) const
{
    MRPT_START

    // Do not update the vehicle pose & its covariance until we have some
    // landmakrs in the map,
    // otherwise, we are imposing a lower bound to the best uncertainty from now
    // on:
    if (size_t(m_xkk.size()) == get_vehicle_size())
    {
        out_skipPrediction = true;
        return;
    }

    CPose2D robotPose(xv[0], xv[1], xv[2]);
    CPose2D odoIncrement(u[0], u[1], u[2]);

    // Pose composition:
    robotPose = robotPose + odoIncrement;

    xv[0] = robotPose.x();
    xv[1] = robotPose.y();
    xv[2] = robotPose.phi();

    MRPT_END
}

/** This virtual function musts calculate the Jacobian F of the prediction
 * model.
 */
void CRangeBearingKFSLAM2D::OnTransitionJacobian(KFMatrix_VxV& F) const
{
    MRPT_START

    // The jacobian of the transition function:  dfv_dxv
    CActionRobotMovement2D::Ptr act2D = m_action->getBestMovementEstimation();
    CActionRobotMovement3D::Ptr act3D =
        m_action->getActionByClass<CActionRobotMovement3D>();

    if (act3D && act2D)
        THROW_EXCEPTION("Both 2D & 3D odometry are present!?!?");

    TPoint2D Ap;

    if (act3D)
    {
        Ap = TPoint2D(CPose2D(act3D->poseChange.mean).asTPose());
    }
    else if (act2D)
    {
        Ap = TPoint2D(act2D->poseChange->getMeanVal().asTPose());
    }
    else
    {
        // No odometry at all:
        F.setIdentity();  // Unit diagonal
        return;
    }

    const kftype cy = cos(m_xkk[2]);
    const kftype sy = sin(m_xkk[2]);

    const kftype Ax = Ap.x;
    const kftype Ay = Ap.y;

    F.setIdentity();  // Unit diagonal

    F(0, 2) = -Ax * sy - Ay * cy;
    F(1, 2) = Ax * cy - Ay * sy;

    MRPT_END
}

/** This virtual function musts calculate de noise matrix of the prediction
 * model.
 */
void CRangeBearingKFSLAM2D::OnTransitionNoise(KFMatrix_VxV& Q) const
{
    MRPT_START

    // The uncertainty of the 2D odometry, projected from the current position:
    CActionRobotMovement2D::Ptr act2D = m_action->getBestMovementEstimation();
    CActionRobotMovement3D::Ptr act3D =
        m_action->getActionByClass<CActionRobotMovement3D>();

    if (act3D && act2D)
        THROW_EXCEPTION("Both 2D & 3D odometry are present!?!?");

    CPosePDFGaussian odoIncr;

    if (!act3D && !act2D)
    {
        // Use constant Q:
        Q.setZero();
        ASSERT_(int(options.stds_Q_no_odo.size()) == Q.cols());
        for (size_t i = 0; i < 3; i++)
            Q(i, i) = square(options.stds_Q_no_odo[i]);
        return;
    }
    else
    {
        if (act2D)
        {
            // 2D odometry:
            odoIncr = CPosePDFGaussian(*act2D->poseChange);
        }
        else
        {
            // 3D odometry:
            odoIncr = CPosePDFGaussian(act3D->poseChange);
        }
    }

    odoIncr.rotateCov(m_xkk[2]);

    Q = odoIncr.cov;

    MRPT_END
}

void CRangeBearingKFSLAM2D::OnObservationModel(
    const std::vector<size_t>& idx_landmarks_to_predict,
    vector_KFArray_OBS& out_predictions) const
{
    MRPT_START

    // Get the sensor pose relative to the robot:
    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");
    const CPose2D sensorPoseOnRobot = CPose2D(obs->sensorLocationOnRobot);

    /* -------------------------------------------
       Equations, obtained using matlab, of the relative 2D position of a
      landmark (xi,yi), relative
          to a robot 2D pose (x0,y0,phi)
        Refer to technical report "6D EKF derivation...", 2008

        x0 y0 phi0         % Robot's 2D pose
        x0s y0s phis      % Sensor's 2D pose relative to robot
        xi yi             % Absolute 2D landmark coordinates:

        Hx : dh_dxv   -> Jacobian of the observation model wrt the robot pose
        Hy : dh_dyi   -> Jacobian of the observation model wrt each landmark
      mean position

        Sizes:
         h:  1x2
         Hx: 2x3
         Hy: 2x2
      ------------------------------------------- */
    // Mean of the prior of the robot pose:
    const CPose2D robotPose(m_xkk[0], m_xkk[1], m_xkk[2]);

    const size_t vehicle_size = get_vehicle_size();
    const size_t feature_size = get_feature_size();

    const CPose2D sensorPoseAbs = robotPose + sensorPoseOnRobot;

    // ---------------------------------------------------
    // Observation prediction
    // ---------------------------------------------------
    const size_t N = idx_landmarks_to_predict.size();
    out_predictions.resize(N);
    for (size_t i = 0; i < N; i++)
    {
        const size_t idx_lm = idx_landmarks_to_predict[i];
        ASSERTDEB_(idx_lm < this->getNumberOfLandmarksInTheMap());

        // Landmark absolute position in the map:
        const kftype xi = m_xkk[vehicle_size + feature_size * idx_lm + 0];
        const kftype yi = m_xkk[vehicle_size + feature_size * idx_lm + 1];

        const double Axi = xi - sensorPoseAbs.x();
        const double Ayi = yi - sensorPoseAbs.y();

        out_predictions[i][0] = sqrt(square(Axi) + square(Ayi));  // Range
        out_predictions[i][1] =
            mrpt::math::wrapToPi(atan2(Ayi, Axi) - sensorPoseAbs.phi());  // Yaw
    }

    MRPT_END
}

void CRangeBearingKFSLAM2D::OnObservationJacobians(
    size_t idx_landmark_to_predict, KFMatrix_OxV& Hx, KFMatrix_OxF& Hy) const
{
    MRPT_START

    // Get the sensor pose relative to the robot:
    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");
    const CPose2D sensorPoseOnRobot = CPose2D(obs->sensorLocationOnRobot);

    /* -------------------------------------------
       Equations, obtained using matlab, of the relative 2D position of a
      landmark (xi,yi), relative
          to a robot 2D pose (x0,y0,phi)
        Refer to technical report "6D EKF derivation...", 2008

        x0 y0 phi0         % Robot's 2D pose
        x0s y0s phis      % Sensor's 2D pose relative to robot
        xi yi             % Absolute 2D landmark coordinates:

        Hx : dh_dxv   -> Jacobian of the observation model wrt the robot pose
        Hy : dh_dyi   -> Jacobian of the observation model wrt each landmark
      mean position

        Sizes:
         h:  1x2
         Hx: 2x3
         Hy: 2x2
      ------------------------------------------- */
    // Mean of the prior of the robot pose:
    const CPose2D robotPose(m_xkk[0], m_xkk[1], m_xkk[2]);

    const size_t vehicle_size = get_vehicle_size();
    const size_t feature_size = get_feature_size();

    // Robot 6D pose:
    const kftype x0 = m_xkk[0];
    const kftype y0 = m_xkk[1];
    const kftype phi0 = m_xkk[2];

    const kftype cphi0 = cos(phi0);
    const kftype sphi0 = sin(phi0);

    // Sensor 2D pose on robot:
    const kftype x0s = sensorPoseOnRobot.x();
    const kftype y0s = sensorPoseOnRobot.y();
    const kftype phis = sensorPoseOnRobot.phi();

    const kftype cphi0s = cos(phi0 + phis);
    const kftype sphi0s = sin(phi0 + phis);

    const CPose2D sensorPoseAbs = robotPose + sensorPoseOnRobot;

    // Landmark absolute position in the map:
    const kftype xi =
        m_xkk[vehicle_size + feature_size * idx_landmark_to_predict + 0];
    const kftype yi =
        m_xkk[vehicle_size + feature_size * idx_landmark_to_predict + 1];

    // ---------------------------------------------------
    // Generate dhi_dxv: A 2x3 block
    // ---------------------------------------------------
    const kftype EXP1 =
        -2 * yi * y0s * cphi0 - 2 * yi * y0 + 2 * xi * y0s * sphi0 -
        2 * xi * x0 - 2 * xi * x0s * cphi0 - 2 * yi * x0s * sphi0 +
        2 * y0s * y0 * cphi0 - 2 * y0s * x0 * sphi0 + 2 * y0 * x0s * sphi0 +
        square(x0) + 2 * x0s * x0 * cphi0 + square(x0s) + square(y0s) +
        square(xi) + square(yi) + square(y0);
    const kftype sqrtEXP1_1 = kftype(1) / sqrt(EXP1);

    const kftype EXP2 = cphi0s * xi + sphi0s * yi - sin(phis) * y0s -
                        y0 * sphi0s - x0s * cos(phis) - x0 * cphi0s;
    const kftype EXP2sq = square(EXP2);

    const kftype EXP3 = -sphi0s * xi + cphi0s * yi - cos(phis) * y0s -
                        y0 * cphi0s + x0s * sin(phis) + x0 * sphi0s;
    const kftype EXP3sq = square(EXP3);

    const kftype EXP4 = kftype(1) / (1 + EXP3sq / EXP2sq);

    Hx(0, 0) = (-xi - sphi0 * y0s + cphi0 * x0s + x0) * sqrtEXP1_1;
    Hx(0, 1) = (-yi + cphi0 * y0s + y0 + sphi0 * x0s) * sqrtEXP1_1;
    Hx(0, 2) = (y0s * xi * cphi0 + y0s * yi * sphi0 - y0 * y0s * sphi0 -
                x0 * y0s * cphi0 + x0s * xi * sphi0 - x0s * yi * cphi0 +
                y0 * x0s * cphi0 - x0s * x0 * sphi0) *
               sqrtEXP1_1;

    Hx(1, 0) = (sphi0s / (EXP2) + (EXP3) / EXP2sq * cphi0s) * EXP4;
    Hx(1, 1) = (-cphi0s / (EXP2) + (EXP3) / EXP2sq * sphi0s) * EXP4;
    Hx(1, 2) =
        ((-cphi0s * xi - sphi0s * yi + y0 * sphi0s + x0 * cphi0s) / (EXP2) -
         (EXP3) / EXP2sq *
             (-sphi0s * xi + cphi0s * yi - y0 * cphi0s + x0 * sphi0s)) *
        EXP4;

    // ---------------------------------------------------
    // Generate dhi_dyi: A 2x2 block
    // ---------------------------------------------------
    Hy(0, 0) = (xi + sphi0 * y0s - cphi0 * x0s - x0) * sqrtEXP1_1;
    Hy(0, 1) = (yi - cphi0 * y0s - y0 - sphi0 * x0s) * sqrtEXP1_1;

    Hy(1, 0) = (-sphi0s / (EXP2) - (EXP3) / EXP2sq * cphi0s) * EXP4;
    Hy(1, 1) = (cphi0s / (EXP2) - (EXP3) / EXP2sq * sphi0s) * EXP4;

    MRPT_END
}

/** This is called between the KF prediction step and the update step, and the
 * application must return the observations and, when applicable, the data
 * association between these observations and the current map.
 *
 * \param out_z N vectors, each for one "observation" of length OBS_SIZE, N
 * being the number of "observations": how many observed landmarks for a map, or
 * just one if not applicable.
 * \param out_data_association An empty vector or, where applicable, a vector
 * where the i'th element corresponds to the position of the observation in the
 * i'th row of out_z within the system state vector (in the range
 * [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and
 * we want to insert it at the end of this KF iteration.
 * \param in_S The full covariance matrix of the observation predictions (i.e.
 * the "innovation covariance matrix"). This is a M·O x M·O matrix with M=length
 * of "in_lm_indices_in_S".
 * \param in_lm_indices_in_S The indices of the map landmarks (range
 * [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.
 *
 *  This method will be called just once for each complete KF iteration.
 * \note It is assumed that the observations are independent, i.e. there are NO
 * cross-covariances between them.
 */
void CRangeBearingKFSLAM2D::OnGetObservationsAndDataAssociation(
    vector_KFArray_OBS& Z, std::vector<int>& data_association,
    const vector_KFArray_OBS& all_predictions, const KFMatrix& S,
    const std::vector<size_t>& lm_indices_in_S, const KFMatrix_OxO& R)
{
    MRPT_START

    constexpr size_t obs_size = get_observation_size();

    // Z: Observations
    CObservationBearingRange::TMeasurementList::const_iterator itObs;

    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");

    const size_t N = obs->sensedData.size();
    Z.resize(N);

    size_t row;
    for (row = 0, itObs = obs->sensedData.begin();
         itObs != obs->sensedData.end(); ++itObs, ++row)
    {
        // Fill one row in Z:
        Z[row][0] = itObs->range;
        Z[row][1] = itObs->yaw;
        ASSERTMSG_(
            itObs->pitch == 0,
            "ERROR: Observation contains pitch!=0 but this is 2D-SLAM!!!");
    }

    // Data association:
    // ---------------------
    data_association.assign(N, -1);  // Initially, all new landmarks

    // For each observed LM:
    std::vector<size_t> obs_idxs_needing_data_assoc;
    obs_idxs_needing_data_assoc.reserve(N);

    {
        std::vector<int>::iterator itDA;
        for (row = 0, itObs = obs->sensedData.begin(),
            itDA = data_association.begin();
             itObs != obs->sensedData.end(); ++itObs, ++itDA, ++row)
        {
            // Fill data asociation: Using IDs!
            if (itObs->landmarkID < 0)
                obs_idxs_needing_data_assoc.push_back(row);
            else
            {
                mrpt::containers::bimap<
                    CLandmark::TLandmarkID, unsigned int>::iterator itID;
                if ((itID = m_IDs.find_key(itObs->landmarkID)) != m_IDs.end())
                    *itDA = itID->second;  // This row in Z corresponds to the
                // i'th map element in the state
                // vector:
            }
        }
    }

    // ---- Perform data association ----
    //  Only for observation indices in "obs_idxs_needing_data_assoc"
    if (obs_idxs_needing_data_assoc.empty())
    {
        // We don't need to do DA:
        m_last_data_association = TDataAssocInfo();

        // Save them for the case the external user wants to access them:
        for (size_t idxObs = 0; idxObs < data_association.size(); idxObs++)
        {
            int da = data_association[idxObs];
            if (da >= 0)
                m_last_data_association.results.associations[idxObs] =
                    data_association[idxObs];
        }

#if STATS_EXPERIMENT  // DEBUG: Generate statistic info
        {
            static CFileOutputStream fC("metric_stats_C.txt", true);
#if STATS_EXPERIMENT_ALSO_NC
            static CFileOutputStream fNC("metric_stats_NC.txt", true);
#endif

            std::vector<int>::iterator itDA;
            size_t idx_obs = 0;
            for (itDA = data_association.begin();
                 itDA != data_association.end(); ++itDA, ++idx_obs)
            {
                if (*itDA == -1) continue;
                const size_t lm_idx_in_map = *itDA;
                size_t valid_idx_pred =
                    find_in_vector(lm_idx_in_map, lm_indices_in_S);

                const KFArray_OBS& obs_mu = Z[idx_obs];

                for (size_t idx_pred = 0; idx_pred < lm_indices_in_S.size();
                     idx_pred++)
                {
                    const KFArray_OBS& lm_mu =
                        all_predictions[lm_indices_in_S[idx_pred]];

                    const size_t base_idx_in_S = obs_size * idx_pred;
                    KFMatrix_OxO lm_cov;
                    S.extractMatrix(base_idx_in_S, base_idx_in_S, lm_cov);

                    double md, log_pdf;
                    mahalanobisDistance2AndLogPDF(
                        lm_mu - obs_mu, lm_cov, md, log_pdf);

                    if (valid_idx_pred == idx_pred)
                        fC.printf("%e %e\n", md, log_pdf);
                    else
                    {
#if STATS_EXPERIMENT_ALSO_NC
                        fNC.printf("%e %e\n", md, log_pdf);
#endif
                    }
                }
            }
        }
#endif
    }
    else
    {
        // Build a Z matrix with the observations that need dat.assoc:
        const size_t nObsDA = obs_idxs_needing_data_assoc.size();

        CMatrixDynamic<kftype> Z_obs_means(nObsDA, obs_size);
        for (size_t i = 0; i < nObsDA; i++)
        {
            const size_t idx = obs_idxs_needing_data_assoc[i];
            for (unsigned k = 0; k < obs_size; k++)
                Z_obs_means(i, k) = Z[idx][k];
        }

        // Vehicle uncertainty
        /*const KFMatrix_VxV Pxx = m_pkk.extractMatrix<get_vehicle_size(),
         * get_vehicle_size()>(0, 0);*/

        // Build predictions:
        // ---------------------------
        const size_t nPredictions = lm_indices_in_S.size();
        m_last_data_association.clear();

        // S is the covariance of the predictions:
        m_last_data_association.Y_pred_covs = S;

        // The means:
        m_last_data_association.Y_pred_means.setSize(nPredictions, obs_size);
        for (size_t q = 0; q < nPredictions; q++)
        {
            const size_t i = lm_indices_in_S[q];
            for (size_t w = 0; w < obs_size; w++)
                m_last_data_association.Y_pred_means(q, w) =
                    all_predictions[i][w];
            m_last_data_association.predictions_IDs.push_back(
                i);  // for the conversion of indices...
        }

        // Do Dat. Assoc :
        // ---------------------------
        if (nPredictions)
        {
            // CMatrixDouble Z_obs_cov = CMatrixDouble(R);

            mrpt::slam::data_association_full_covariance(
                Z_obs_means,  // Z_obs_cov,
                m_last_data_association.Y_pred_means,
                m_last_data_association.Y_pred_covs,
                m_last_data_association.results, options.data_assoc_method,
                options.data_assoc_metric, options.data_assoc_IC_chi2_thres,
                true,  // Use KD-tree
                m_last_data_association.predictions_IDs,
                options.data_assoc_IC_metric,
                options.data_assoc_IC_ml_threshold);

            // Return pairings to the main KF algorithm:
            for (auto it = m_last_data_association.results.associations.begin();
                 it != m_last_data_association.results.associations.end(); ++it)
                data_association[it->first] = it->second;
        }
    }
    // ---- End of data association ----

    MRPT_END
}

/** This virtual function musts normalize the state vector and covariance matrix
 * (only if its necessary).
 */
void CRangeBearingKFSLAM2D::OnNormalizeStateVector()
{
    // Check angles:
    mrpt::math::wrapToPiInPlace(m_xkk[2]);
}

/*---------------------------------------------------------------
                        loadOptions
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::loadOptions(
    const mrpt::config::CConfigFileBase& ini)
{
    // Main
    options.loadFromConfigFile(ini, "RangeBearingKFSLAM");
    KF_options.loadFromConfigFile(ini, "RangeBearingKFSLAM_KalmanFilter");
}

/*---------------------------------------------------------------
                        loadOptions
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::TOptions::loadFromConfigFile(
    const mrpt::config::CConfigFileBase& source, const std::string& section)
{
    stds_Q_no_odo[2] = RAD2DEG(stds_Q_no_odo[2]);

    source.read_vector(section, "stds_Q_no_odo", stds_Q_no_odo, stds_Q_no_odo);
    ASSERT_(stds_Q_no_odo.size() == 3);
    stds_Q_no_odo[2] = DEG2RAD(stds_Q_no_odo[2]);

    std_sensor_range =
        source.read_float(section, "std_sensor_range", std_sensor_range);
    std_sensor_yaw = DEG2RAD(source.read_float(
        section, "std_sensor_yaw_deg", RAD2DEG(std_sensor_yaw)));

    MRPT_LOAD_CONFIG_VAR(quantiles_3D_representation, float, source, section);
    MRPT_LOAD_CONFIG_VAR(create_simplemap, bool, source, section);

    data_assoc_method = source.read_enum<TDataAssociationMethod>(
        section, "data_assoc_method", data_assoc_method);
    data_assoc_metric = source.read_enum<TDataAssociationMetric>(
        section, "data_assoc_metric", data_assoc_metric);
    data_assoc_IC_metric = source.read_enum<TDataAssociationMetric>(
        section, "data_assoc_IC_metric", data_assoc_IC_metric);

    MRPT_LOAD_CONFIG_VAR(data_assoc_IC_chi2_thres, double, source, section);
    MRPT_LOAD_CONFIG_VAR(data_assoc_IC_ml_threshold, double, source, section);
}

/*---------------------------------------------------------------
                        Constructor
  ---------------------------------------------------------------*/
CRangeBearingKFSLAM2D::TOptions::TOptions()
    : stds_Q_no_odo(3, 0),

      std_sensor_yaw(DEG2RAD(0.5f))

{
    stds_Q_no_odo[0] = 0.10f;
    stds_Q_no_odo[1] = 0.10f;
    stds_Q_no_odo[2] = DEG2RAD(4.0f);
}

/*---------------------------------------------------------------
                        dumpToTextStream
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::TOptions::dumpToTextStream(std::ostream& out) const
{
    using namespace mrpt::typemeta;

    out << "\n----------- [CRangeBearingKFSLAM2D::TOptions] ------------ \n\n";

    out << mrpt::format(
        "data_assoc_method                       = %s\n",
        TEnumType<TDataAssociationMethod>::value2name(data_assoc_method)
            .c_str());
    out << mrpt::format(
        "data_assoc_metric                       = %s\n",
        TEnumType<TDataAssociationMetric>::value2name(data_assoc_metric)
            .c_str());
    out << mrpt::format(
        "data_assoc_IC_chi2_thres                = %.06f\n",
        data_assoc_IC_chi2_thres);
    out << mrpt::format(
        "data_assoc_IC_metric                    = %s\n",
        TEnumType<TDataAssociationMetric>::value2name(data_assoc_IC_metric)
            .c_str());
    out << mrpt::format(
        "data_assoc_IC_ml_threshold              = %.06f\n",
        data_assoc_IC_ml_threshold);

    out << "\n";
}

void CRangeBearingKFSLAM2D::OnInverseObservationModel(
    const KFArray_OBS& in_z, KFArray_FEAT& yn, KFMatrix_FxV& dyn_dxv,
    KFMatrix_FxO& dyn_dhn) const
{
    MRPT_START

    /* -------------------------------------------
       Equations, obtained using matlab

        x0 y0 phi0      % Robot's 2D pose
        x0s y0s phis    % Sensor's 2D pose relative to robot
        hr ha           % Observation hn: range, yaw

        xi yi           % Absolute 2D landmark coordinates:

        dyn_dxv   -> Jacobian of the inv. observation model wrt the robot pose
        dyn_dhn   -> Jacobian of the inv. observation model wrt each landmark
      observation

        Sizes:
         hn:      1x2  <--
         yn:      1x2  -->
         dyn_dxv: 2x3  -->
         dyn_dhn: 2x2  -->
      ------------------------------------------- */

    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");
    const CPose2D sensorPoseOnRobot = CPose2D(obs->sensorLocationOnRobot);

    // Mean of the prior of the robot pose:
    const TPose2D robotPose(m_xkk[0], m_xkk[1], m_xkk[2]);

    // Robot 2D pose:
    const kftype x0 = m_xkk[0];
    const kftype y0 = m_xkk[1];
    const kftype phi0 = m_xkk[2];

    const kftype cphi0 = cos(phi0);
    const kftype sphi0 = sin(phi0);

    // Sensor 6D pose on robot:
    const kftype x0s = sensorPoseOnRobot.x();
    const kftype y0s = sensorPoseOnRobot.y();
    const kftype phis = sensorPoseOnRobot.phi();

    const kftype hr = in_z[0];
    const kftype ha = in_z[1];

    const kftype cphi_0sa = cos(phi0 + phis + ha);
    const kftype sphi_0sa = sin(phi0 + phis + ha);

    // Compute the mean 2D absolute coordinates:
    yn[0] = hr * cphi_0sa + cphi0 * x0s - sphi0 * y0s + x0;
    yn[1] = hr * sphi_0sa + sphi0 * x0s + cphi0 * y0s + y0;

    // Jacobian wrt xv:
    dyn_dxv(0, 0) = 1;
    dyn_dxv(0, 1) = 0;
    dyn_dxv(0, 2) = -hr * sphi_0sa - sphi0 * x0s - cphi0 * y0s;

    dyn_dxv(1, 0) = 0;
    dyn_dxv(1, 1) = 1;
    dyn_dxv(1, 2) = hr * cphi_0sa + cphi0 * x0s - sphi0 * y0s;

    // Jacobian wrt hn:
    dyn_dhn(0, 0) = cphi_0sa;
    dyn_dhn(0, 1) = -hr * sphi_0sa;

    dyn_dhn(1, 0) = sphi_0sa;
    dyn_dhn(1, 1) = hr * cphi_0sa;

    MRPT_END
}

/** If applicable to the given problem, do here any special handling of adding a
 * new landmark to the map.
 * \param in_obsIndex The index of the observation whose inverse sensor is to
 * be computed. It corresponds to the row in in_z where the observation can be
 * found.
 * \param in_idxNewFeat The index that this new feature will have in the state
 * vector (0:just after the vehicle state, 1: after that,...). Save this number
 * so data association can be done according to these indices.
 * \sa OnInverseObservationModel
 */
void CRangeBearingKFSLAM2D::OnNewLandmarkAddedToMap(
    const size_t in_obsIdx, const size_t in_idxNewFeat)
{
    MRPT_START

    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");

    // ----------------------------------------------
    // introduce in the lists of ID<->index in map:
    // ----------------------------------------------
    ASSERT_(in_obsIdx < obs->sensedData.size());
    if (obs->sensedData[in_obsIdx].landmarkID >= 0)
    {
        // The sensor provides us a LM ID... use it:
        m_IDs.insert(obs->sensedData[in_obsIdx].landmarkID, in_idxNewFeat);
    }
    else
    {
        // Features do not have IDs... use indices:
        m_IDs.insert(in_idxNewFeat, in_idxNewFeat);
    }

    m_last_data_association.newly_inserted_landmarks[in_obsIdx] =
        in_idxNewFeat;  // Just for stats, etc...

    MRPT_END
}

/*---------------------------------------------------------------
                        getAs3DObject
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::getAs3DObject(
    mrpt::opengl::CSetOfObjects::Ptr& outObj) const
{
    outObj->clear();

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

    // 2D ellipsoid for robot pose:
    CPoint2DPDFGaussian pointGauss;
    pointGauss.mean.x(m_xkk[0]);
    pointGauss.mean.y(m_xkk[1]);
    pointGauss.cov = m_pkk.blockCopy<2, 2>(0, 0);

    {
        auto ellip = opengl::CEllipsoid2D::Create();

        ellip->setPose(pointGauss.mean);
        ellip->setCovMatrix(pointGauss.cov);
        ellip->enableDrawSolid3D(false);
        ellip->setQuantiles(options.quantiles_3D_representation);
        ellip->setColor(1, 0, 0);

        outObj->insert(ellip);
    }

    // 2D ellipsoids for landmarks:
    const size_t nLMs = (m_xkk.size() - 3) / 2;
    for (size_t i = 0; i < nLMs; i++)
    {
        pointGauss.mean.x(m_xkk[3 + 2 * i + 0]);
        pointGauss.mean.y(m_xkk[3 + 2 * i + 1]);
        pointGauss.cov = m_pkk.blockCopy<2, 2>(3 + 2 * i, 3 + 2 * i);

        auto ellip = opengl::CEllipsoid2D::Create();

        ellip->setName(format("%u", static_cast<unsigned int>(i)));
        ellip->enableShowName(true);
        ellip->setPose(pointGauss.mean);
        ellip->setCovMatrix(pointGauss.cov);
        ellip->enableDrawSolid3D(false);
        ellip->setQuantiles(options.quantiles_3D_representation);

        ellip->setColor(0, 0, 1);

        outObj->insert(ellip);
    }
}

/*---------------------------------------------------------------
              saveMapAndPathRepresentationAsMATLABFile
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM2D::saveMapAndPath2DRepresentationAsMATLABFile(
    const string& fil, float stdCount, const string& styleLandmarks,
    const string& stylePath, const string& styleRobot) const
{
    FILE* f = os::fopen(fil.c_str(), "wt");
    if (!f) return;

    CMatrixDouble cov(2, 2);
    CVectorDouble mean(2);

    // Header:
    os::fprintf(
        f,
        "%%--------------------------------------------------------------------"
        "\n");
    os::fprintf(f, "%% File automatically generated using the MRPT method:\n");
    os::fprintf(
        f,
        "%% "
        "'CRangeBearingKFSLAM2D::saveMapAndPath2DRepresentationAsMATLABFile'"
        "\n");
    os::fprintf(f, "%%\n");
    os::fprintf(f, "%%                        ~ MRPT ~\n");
    os::fprintf(
        f, "%%  Jose Luis Blanco Claraco, University of Malaga @ 2008\n");
    os::fprintf(f, "%%      https://www.mrpt.org/     \n");
    os::fprintf(
        f,
        "%%--------------------------------------------------------------------"
        "\n");

    // Main code:
    os::fprintf(f, "hold on;\n\n");

    size_t i, nLMs = (m_xkk.size() - get_vehicle_size()) / get_feature_size();

    for (i = 0; i < nLMs; i++)
    {
        size_t idx = get_vehicle_size() + i * get_feature_size();

        cov(0, 0) = m_pkk(idx + 0, idx + 0);
        cov(1, 1) = m_pkk(idx + 1, idx + 1);
        cov(0, 1) = cov(1, 0) = m_pkk(idx + 0, idx + 1);

        mean[0] = m_xkk[idx + 0];
        mean[1] = m_xkk[idx + 1];

        // Command to draw the 2D ellipse:
        os::fprintf(
            f, "%s",
            math::MATLAB_plotCovariance2D(cov, mean, stdCount, styleLandmarks)
                .c_str());
    }

    // Now: the robot path:
    // ------------------------------
    if (m_SFs.size())
    {
        os::fprintf(f, "\nROB_PATH=[");
        for (i = 0; i < m_SFs.size(); i++)
        {
            CSensoryFrame::Ptr dummySF;
            CPose3DPDF::Ptr pdf3D;
            m_SFs.get(i, pdf3D, dummySF);

            CPose3D p;
            pdf3D->getMean(p);
            CPoint3D pnt3D(p);  // 6D -> 3D only

            os::fprintf(f, "%.04f %.04f", pnt3D.x(), pnt3D.y());
            if (i < (m_SFs.size() - 1)) os::fprintf(f, ";");
        }
        os::fprintf(f, "];\n");

        os::fprintf(
            f, "plot(ROB_PATH(:,1),ROB_PATH(:,2),'%s');\n", stylePath.c_str());
    }

    // The robot pose:
    cov(0, 0) = m_pkk(0, 0);
    cov(1, 1) = m_pkk(1, 1);
    cov(0, 1) = cov(1, 0) = m_pkk(0, 1);

    mean[0] = m_xkk[0];
    mean[1] = m_xkk[1];

    os::fprintf(
        f, "%s",
        math::MATLAB_plotCovariance2D(cov, mean, stdCount, styleRobot).c_str());

    os::fprintf(f, "\naxis equal;\n");
    os::fclose(f);
}

/** Computes A=A-B, which may need to be re-implemented depending on the
 * topology of the individual scalar components (eg, angles).
 */
void CRangeBearingKFSLAM2D::OnSubstractObservationVectors(
    KFArray_OBS& A, const KFArray_OBS& B) const
{
    A[0] -= B[0];
    A[1] -= B[1];
    mrpt::math::wrapToPiInPlace(A[1]);
}

/** Return the observation NOISE covariance matrix, that is, the model of the
 * Gaussian additive noise of the sensor.
 * \param out_R The noise covariance matrix. It might be non diagonal, but
 * it'll usually be.
 */
void CRangeBearingKFSLAM2D::OnGetObservationNoise(KFMatrix_OxO& out_R) const
{
    out_R(0, 0) = square(options.std_sensor_range);
    out_R(1, 1) = square(options.std_sensor_yaw);
}

/** This will be called before OnGetObservationsAndDataAssociation to allow the
 * application to reduce the number of covariance landmark predictions to be
 * made.
 *  For example, features which are known to be "out of sight" shouldn't be
 * added to the output list to speed up the calculations.
 * \param in_all_prediction_means The mean of each landmark predictions; the
 * computation or not of the corresponding covariances is what we're trying to
 * determined with this method.
 * \param out_LM_indices_to_predict The list of landmark indices in the map
 * [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.
 * \note This is not a pure virtual method, so it should be implemented only if
 * desired. The default implementation returns a vector with all the landmarks
 * in the map.
 * \sa OnGetObservations, OnDataAssociation
 */
void CRangeBearingKFSLAM2D::OnPreComputingPredictions(
    const vector_KFArray_OBS& prediction_means,
    std::vector<size_t>& out_LM_indices_to_predict) const
{
    CObservationBearingRange::Ptr obs =
        m_SF->getObservationByClass<CObservationBearingRange>();
    ASSERTMSG_(
        obs,
        "*ERROR*: This method requires an observation of type "
        "CObservationBearingRange");

    const double sensor_max_range = obs->maxSensorDistance;
    const double fov_yaw = obs->fieldOfView_yaw;

    const double max_vehicle_loc_uncertainty =
        4 * std::sqrt(m_pkk(0, 0) + m_pkk(1, 1));
    const double max_vehicle_ang_uncertainty = 4 * std::sqrt(m_pkk(2, 2));

    out_LM_indices_to_predict.clear();
    for (size_t i = 0; i < prediction_means.size(); i++)
#if (!STATS_EXPERIMENT)  // In the experiment we force errors too far and some
        // predictions are out of range, so just generate all
        // of them:
        if (prediction_means[i][0] <
                (1.5 + sensor_max_range + max_vehicle_loc_uncertainty +
                 4 * options.std_sensor_range) &&
            fabs(prediction_means[i][1]) <
                (20.0_deg + 0.5 * fov_yaw + max_vehicle_ang_uncertainty +
                 4 * options.std_sensor_yaw))
#endif
        {
            out_LM_indices_to_predict.push_back(i);
        }
}

/** Only called if using a numeric approximation of the transition Jacobian,
 * this method must return the increments in each dimension of the vehicle state
 * vector while estimating the Jacobian.
 */
void CRangeBearingKFSLAM2D::OnTransitionJacobianNumericGetIncrements(
    KFArray_VEH& out_increments) const
{
    for (size_t i = 0; i < get_vehicle_size(); i++) out_increments[i] = 1e-6;
}

/** Only called if using a numeric approximation of the observation Jacobians,
 * this method must return the increments in each dimension of the vehicle state
 * vector while estimating the Jacobian.
 */
void CRangeBearingKFSLAM2D::OnObservationJacobiansNumericGetIncrements(
    KFArray_VEH& out_veh_increments, KFArray_FEAT& out_feat_increments) const
{
    for (size_t i = 0; i < get_vehicle_size(); i++)
        out_veh_increments[i] = 1e-6;
    for (size_t i = 0; i < get_feature_size(); i++)
        out_feat_increments[i] = 1e-6;
}