/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2022, 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
//

// ----------------------------------------------------------------------------------------
// For the theory behind this implementation, see the technical report in:
//
//            https://www.mrpt.org/6D-SLAM
// ----------------------------------------------------------------------------------------

#include <mrpt/math/ops_containers.h>
#include <mrpt/math/utils.h>
#include <mrpt/math/wrap2pi.h>
#include <mrpt/obs/CActionRobotMovement3D.h>
#include <mrpt/opengl/CEllipsoid3D.h>
#include <mrpt/opengl/CSetOfObjects.h>
#include <mrpt/opengl/stock_objects.h>
#include <mrpt/poses/CPose3DQuatPDFGaussian.h>
#include <mrpt/poses/CPosePDF.h>
#include <mrpt/poses/CPosePDFGaussian.h>
#include <mrpt/slam/CRangeBearingKFSLAM.h>
#include <mrpt/system/CTicTac.h>
#include <mrpt/system/os.h>

#include <Eigen/Dense>

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

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

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

	// -----------------------
	// INIT KF STATE
	m_xkk.assign(get_vehicle_size(), 0);  // State: 6D pose (x,y,z)=(0,0,0)
	m_xkk[3] = 1.0;	 // (qr,qx,qy,qz)=(1,0,0,0)

	// Initial cov:  nullptr diagonal -> perfect knowledge.
	m_pkk.setSize(get_vehicle_size(), get_vehicle_size());
	m_pkk.setZero();
	// -----------------------

	// Use SF-based matching (faster & easier for bearing-range observations
	// with ID).
	mapPartitioner.options.simil_method = smOBSERVATION_OVERLAP;
}

/*---------------------------------------------------------------
							Destructor
  ---------------------------------------------------------------*/
CRangeBearingKFSLAM::~CRangeBearingKFSLAM() = default;
/*---------------------------------------------------------------
							getCurrentRobotPose
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::getCurrentRobotPose(
	CPose3DQuatPDFGaussian& out_robotPose) const
{
	MRPT_START

	ASSERT_(m_xkk.size() >= 7);

	// Copy xyz+quat: (explicitly unroll the loop)
	out_robotPose.mean.m_coords[0] = m_xkk[0];
	out_robotPose.mean.m_coords[1] = m_xkk[1];
	out_robotPose.mean.m_coords[2] = m_xkk[2];
	out_robotPose.mean.m_quat[0] = m_xkk[3];
	out_robotPose.mean.m_quat[1] = m_xkk[4];
	out_robotPose.mean.m_quat[2] = m_xkk[5];
	out_robotPose.mean.m_quat[3] = m_xkk[6];

	// and cov:
	out_robotPose.cov = m_pkk.blockCopy<7, 7>(0, 0);

	MRPT_END
}

/*---------------------------------------------------------------
							getCurrentRobotPoseMean
  ---------------------------------------------------------------*/
mrpt::poses::CPose3DQuat CRangeBearingKFSLAM::getCurrentRobotPoseMean() const
{
	CPose3DQuat q(mrpt::math::UNINITIALIZED_QUATERNION);
	ASSERTDEB_(m_xkk.size() >= 7);
	// Copy xyz+quat: (explicitly unroll the loop)
	q.m_coords[0] = m_xkk[0];
	q.m_coords[1] = m_xkk[1];
	q.m_coords[2] = m_xkk[2];
	q.m_quat[0] = m_xkk[3];
	q.m_quat[1] = m_xkk[4];
	q.m_quat[2] = m_xkk[5];
	q.m_quat[3] = m_xkk[6];

	return q;
}

/*---------------------------------------------------------------
							getCurrentState
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::getCurrentState(
	CPose3DQuatPDFGaussian& out_robotPose,
	std::vector<TPoint3D>& out_landmarksPositions,
	std::map<unsigned int, CLandmark::TLandmarkID>& out_landmarkIDs,
	CVectorDouble& out_fullState, CMatrixDouble& out_fullCovariance) const
{
	MRPT_START

	ASSERT_(size_t(m_xkk.size()) >= get_vehicle_size());

	// Copy xyz+quat: (explicitly unroll the loop)
	out_robotPose.mean.m_coords[0] = m_xkk[0];
	out_robotPose.mean.m_coords[1] = m_xkk[1];
	out_robotPose.mean.m_coords[2] = m_xkk[2];
	out_robotPose.mean.m_quat[0] = m_xkk[3];
	out_robotPose.mean.m_quat[1] = m_xkk[4];
	out_robotPose.mean.m_quat[2] = m_xkk[5];
	out_robotPose.mean.m_quat[3] = m_xkk[6];

	// and cov:
	out_robotPose.cov = m_pkk.blockCopy<7, 7>(0, 0);

	// Landmarks:
	ASSERT_(((m_xkk.size() - get_vehicle_size()) % get_feature_size()) == 0);
	size_t i, nLMs = (m_xkk.size() - get_vehicle_size()) / get_feature_size();
	out_landmarksPositions.resize(nLMs);
	for (i = 0; i < nLMs; i++)
	{
		out_landmarksPositions[i].x =
			m_xkk[get_vehicle_size() + i * get_feature_size() + 0];
		out_landmarksPositions[i].y =
			m_xkk[get_vehicle_size() + i * get_feature_size() + 1];
		out_landmarksPositions[i].z =
			m_xkk[get_vehicle_size() + i * get_feature_size() + 2];
	}  // 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 CRangeBearingKFSLAM::processActionObservation(
	CActionCollection::Ptr& action, CSensoryFrame::Ptr& SF)
{
	MRPT_START

	m_action = action;
	m_SF = SF;

	// Sanity check:
	ASSERT_(
		m_IDs.size() ==
		(m_pkk.cols() - get_vehicle_size()) / get_feature_size());

	// ===================================================================================================================
	// 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
	// =============================================================
	CPose3DQuatPDFGaussian q(UNINITIALIZED_QUATERNION);
	this->getCurrentRobotPose(q);
	CPose3DPDFGaussian::Ptr auxPosePDF = CPose3DPDFGaussian::Create(q);

	if (options.create_simplemap)
	{ m_SFs.insert(CPose3DPDF::Ptr(auxPosePDF), SF); }

	// =============================================================
	//  UPDATE THE PARTITION GRAPH EXPERIMENT
	// =============================================================
	if (options.doPartitioningExperiment)
	{
		if (options.partitioningMethod == 0)
		{
			// Use spectral-graph technique:
			mapPartitioner.addMapFrame(*SF, *auxPosePDF);

			vector<std::vector<uint32_t>> partitions;
			mapPartitioner.updatePartitions(partitions);
			m_lastPartitionSet = partitions;
		}
		else
		{
			// Fixed partitions every K observations:
			vector<std::vector<uint32_t>> partitions;
			std::vector<uint32_t> tmpCluster;

			ASSERT_(options.partitioningMethod > 1);
			size_t N = options.partitioningMethod;

			for (size_t i = 0; i < m_SFs.size(); i++)
			{
				tmpCluster.push_back(i);
				if ((i % N) == 0)
				{
					partitions.push_back(tmpCluster);
					tmpCluster.clear();
					tmpCluster.push_back(i);  // This observation "i" is shared
					// between both clusters
				}
			}
			m_lastPartitionSet = partitions;
		}

		printf(
			"Partitions: %u\n",
			static_cast<unsigned>(m_lastPartitionSet.size()));
	}

	MRPT_END
}

/** Return the last odometry, as a pose increment.
 */
CPose3DQuat CRangeBearingKFSLAM::getIncrementFromOdometry() const
{
	CActionRobotMovement2D::Ptr actMov2D =
		m_action->getBestMovementEstimation();
	CActionRobotMovement3D::Ptr actMov3D =
		m_action->getActionByClass<CActionRobotMovement3D>();
	if (actMov3D && !options.force_ignore_odometry)
	{ return CPose3DQuat(actMov3D->poseChange.mean); }
	else if (actMov2D && !options.force_ignore_odometry)
	{
		CPose2D estMovMean;
		actMov2D->poseChange->getMean(estMovMean);
		return CPose3DQuat(CPose3D(estMovMean));
	}
	else
	{
		return CPose3DQuat();
	}
}

/** Must return the action vector u.
 * \param out_u The action vector which will be passed to OnTransitionModel
 */
void CRangeBearingKFSLAM::OnGetAction(KFArray_ACT& u) const
{
	// Get odometry estimation:
	const CPose3DQuat theIncr = getIncrementFromOdometry();

	for (KFArray_ACT::Index i = 0;
		 i < static_cast<KFArray_ACT::Index>(u.size()); i++)
		u[i] = theIncr[i];
}

/** This virtual function musts implement the prediction model of the Kalman
 * filter.
 */
void CRangeBearingKFSLAM::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
	// landmarks 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; }

	// Current pose: copy xyz+quat
	CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// Increment pose: copy xyz+quat (explicitly unroll the loop)
	CPose3DQuat odoIncrement(UNINITIALIZED_QUATERNION);
	odoIncrement.m_coords[0] = u[0];
	odoIncrement.m_coords[1] = u[1];
	odoIncrement.m_coords[2] = u[2];
	odoIncrement.m_quat[0] = u[3];
	odoIncrement.m_quat[1] = u[4];
	odoIncrement.m_quat[2] = u[5];
	odoIncrement.m_quat[3] = u[6];

	// Pose composition:
	robotPose += odoIncrement;

	// Output:
	for (size_t i = 0; i < xv.SizeAtCompileTime; i++)
		xv[i] = robotPose[i];

	MRPT_END
}

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

	// Current pose: copy xyz+quat
	CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// Odometry:
	const CPose3DQuat theIncr = getIncrementFromOdometry();

	// Compute jacobians:
	CMatrixDouble77 df_du(UNINITIALIZED_MATRIX);

	CPose3DQuatPDF::jacobiansPoseComposition(
		robotPose,	// x
		theIncr,  // u
		F,	// df_dx,
		df_du);

	MRPT_END
}

/** This virtual function musts calculate de noise matrix of the prediction
 * model.
 */
void CRangeBearingKFSLAM::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!?!?");

	CPose3DQuatPDFGaussian odoIncr;

	if ((!act3D && !act2D) || options.force_ignore_odometry)
	{
		// Use constant Q:
		Q.setZero();
		ASSERT_(size_t(options.stds_Q_no_odo.size()) == size_t(Q.cols()));
		for (size_t i = 0; i < get_vehicle_size(); i++)
			Q(i, i) = square(options.stds_Q_no_odo[i]);
		return;
	}
	else
	{
		if (act2D)
		{
			// 2D odometry:
			CPosePDFGaussian odoIncr2D;
			odoIncr2D.copyFrom(*act2D->poseChange);
			odoIncr = CPose3DQuatPDFGaussian(CPose3DPDFGaussian(odoIncr2D));
		}
		else
		{
			// 3D odometry:
			odoIncr = CPose3DQuatPDFGaussian(act3D->poseChange);
		}
	}

	odoIncr.cov(2, 2) += square(options.std_odo_z_additional);

	// Current pose: copy xyz+quat
	CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// Transform from odometry increment to "relative to the robot":
	odoIncr.changeCoordinatesReference(robotPose);

	Q = odoIncr.cov;

	MRPT_END
}

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

	// Mean of the prior of the robot pose:
	CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// 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 CPose3DQuat sensorPoseOnRobot =
		CPose3DQuat(obs->sensorLocationOnRobot);

	/* -------------------------------------------
	   Equations, obtained using matlab, of the relative 3D position of a
	  landmark (xi,yi,zi), relative
		  to a robot 6D pose (x0,y0,z0,y,p,r)
		Refer to technical report "6D EKF derivation...", 2008

		x0 y0 z0 y p r         % Robot's 6D pose
		x0s y0s z0s ys ps rs   % Sensor's 6D pose relative to robot
		xi yi zi % Absolute 3D 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:  Lx3
		 Hx: 3Lx6
		 Hy: 3Lx3

		  L=# of landmarks in the map (ALL OF THEM)
	  ------------------------------------------- */

	const size_t vehicle_size = get_vehicle_size();
	// const size_t  obs_size  = get_observation_size();
	const size_t feature_size = get_feature_size();

	const CPose3DQuat sensorPoseAbs = robotPose + sensorPoseOnRobot;

	const size_t N = idx_landmarks_to_predict.size();
	out_predictions.resize(N);
	for (size_t i = 0; i < N; i++)
	{
		const size_t row_in = feature_size * idx_landmarks_to_predict[i];

		// Landmark absolute 3D position in the map:
		const TPoint3D mapEst(
			m_xkk[vehicle_size + row_in + 0], m_xkk[vehicle_size + row_in + 1],
			m_xkk[vehicle_size + row_in + 2]);

		// Generate Range, yaw, pitch
		// ---------------------------------------------------
		sensorPoseAbs.sphericalCoordinates(
			mapEst,
			out_predictions[i][0],	// range
			out_predictions[i][1],	// yaw
			out_predictions[i][2]  // pitch
		);
	}

	MRPT_END
}

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

	// Mean of the prior of the robot pose:
	const CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// 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 CPose3DQuat sensorPoseOnRobot =
		CPose3DQuat(obs->sensorLocationOnRobot);

	const size_t vehicle_size = get_vehicle_size();
	// const size_t  obs_size  = get_observation_size();
	const size_t feature_size = get_feature_size();

	// Compute the jacobians, needed below:
	// const CPose3DQuat  sensorPoseAbs= robotPose + sensorPoseOnRobot;
	CPose3DQuat sensorPoseAbs(UNINITIALIZED_QUATERNION);
	CMatrixFixed<kftype, 7, 7> H_senpose_vehpose(UNINITIALIZED_MATRIX);
	CMatrixFixed<kftype, 7, 7> H_senpose_senrelpose(
		UNINITIALIZED_MATRIX);	// Not actually used

	CPose3DQuatPDF::jacobiansPoseComposition(
		robotPose, sensorPoseOnRobot, H_senpose_vehpose, H_senpose_senrelpose,
		&sensorPoseAbs);

	const size_t row_in = feature_size * idx_landmark_to_predict;

	// Landmark absolute 3D position in the map:
	const TPoint3D mapEst(
		m_xkk[vehicle_size + row_in + 0], m_xkk[vehicle_size + row_in + 1],
		m_xkk[vehicle_size + row_in + 2]);

	// The Jacobian wrt the sensor pose must be transformed later on:
	KFMatrix_OxV Hx_sensor;
	double obsData[3];
	sensorPoseAbs.sphericalCoordinates(
		mapEst,
		obsData[0],	 // range
		obsData[1],	 // yaw
		obsData[2],	 // pitch
		&Hy, &Hx_sensor);

	// Chain rule: Hx = d sensorpose / d vehiclepose   * Hx_sensor
	Hx = Hx_sensor * H_senpose_vehpose;

	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 CRangeBearingKFSLAM::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

	// static const size_t vehicle_size = get_vehicle_size();
	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;
		Z[row][2] = itObs->pitch;
	}

	// 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];
		}
	}
	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
		// KFMatrix_VxV Pxx = m_pkk.extractMatrix<7, 7>(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, 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 CRangeBearingKFSLAM::OnNormalizeStateVector()
{
	MRPT_START

	// m_xkk[3:6] must be a normalized quaternion:
	const double T = std::sqrt(
		square(m_xkk[3]) + square(m_xkk[4]) + square(m_xkk[5]) +
		square(m_xkk[6]));
	ASSERTMSG_(T > 0, "Vehicle pose quaternion norm is not >0!!");

	const double T_ = (m_xkk[3] < 0 ? -1.0 : 1.0) / T;	// qr>=0
	m_xkk[3] *= T_;
	m_xkk[4] *= T_;
	m_xkk[5] *= T_;
	m_xkk[6] *= T_;

	MRPT_END
}

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

/*---------------------------------------------------------------
						loadOptions
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::TOptions::loadFromConfigFile(
	const mrpt::config::CConfigFileBase& source, const std::string& section)
{
	source.read_vector(section, "stds_Q_no_odo", stds_Q_no_odo, stds_Q_no_odo);
	ASSERT_(stds_Q_no_odo.size() == 7);
	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)));
	std_sensor_pitch = DEG2RAD(source.read_float(
		section, "std_sensor_pitch_deg", RAD2DEG(std_sensor_pitch)));

	std_odo_z_additional = source.read_float(
		section, "std_odo_z_additional", std_odo_z_additional);

	MRPT_LOAD_CONFIG_VAR(doPartitioningExperiment, bool, source, section);
	MRPT_LOAD_CONFIG_VAR(partitioningMethod, int, source, section);

	MRPT_LOAD_CONFIG_VAR(create_simplemap, bool, source, section);

	MRPT_LOAD_CONFIG_VAR(force_ignore_odometry, 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);

	MRPT_LOAD_CONFIG_VAR(quantiles_3D_representation, float, source, section);
}

/*---------------------------------------------------------------
						Constructor
  ---------------------------------------------------------------*/
CRangeBearingKFSLAM::TOptions::TOptions()
	: stds_Q_no_odo(get_vehicle_size(), 0),

	  std_sensor_yaw(DEG2RAD(0.2f)),
	  std_sensor_pitch(DEG2RAD(0.2f))

{
	stds_Q_no_odo[0] = stds_Q_no_odo[1] = stds_Q_no_odo[2] = 0.10f;
	stds_Q_no_odo[3] = stds_Q_no_odo[4] = stds_Q_no_odo[5] = stds_Q_no_odo[6] =
		0.05f;
}

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

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

	out << mrpt::format(
		"doPartitioningExperiment                = %c\n",
		doPartitioningExperiment ? 'Y' : 'N');
	out << mrpt::format(
		"partitioningMethod                      = %i\n", partitioningMethod);
	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 CRangeBearingKFSLAM::OnInverseObservationModel(
	const KFArray_OBS& in_z, KFArray_FEAT& yn, KFMatrix_FxV& dyn_dxv,
	KFMatrix_FxO& dyn_dhn) const
{
	MRPT_START

	CObservationBearingRange::Ptr obs =
		m_SF->getObservationByClass<CObservationBearingRange>();
	ASSERTMSG_(
		obs,
		"*ERROR*: This method requires an observation of type "
		"CObservationBearingRange");
	const CPose3DQuat sensorPoseOnRobot =
		CPose3DQuat(obs->sensorLocationOnRobot);

	// Mean of the prior of the robot pose:
	const CPose3DQuat robotPose = getCurrentRobotPoseMean();

	// const CPose3DQuat  sensorPoseAbs= robotPose + sensorPoseOnRobot;
	CPose3DQuat sensorPoseAbs(UNINITIALIZED_QUATERNION);
	CMatrixFixed<kftype, 7, 7> dsensorabs_dvehpose(UNINITIALIZED_MATRIX);
	CMatrixFixed<kftype, 7, 7> dsensorabs_dsenrelpose(
		UNINITIALIZED_MATRIX);	// Not actually used

	CPose3DQuatPDF::jacobiansPoseComposition(
		robotPose, sensorPoseOnRobot, dsensorabs_dvehpose,
		dsensorabs_dsenrelpose, &sensorPoseAbs);

	kftype hn_r = in_z[0];
	kftype hn_y = in_z[1];
	kftype hn_p = in_z[2];

	kftype chn_y = cos(hn_y);
	kftype shn_y = sin(hn_y);
	kftype chn_p = cos(hn_p);
	kftype shn_p = sin(hn_p);

	/* -------------------------------------------
		syms H h_range h_yaw h_pitch real;
		H=[ h_range ; h_yaw ; h_pitch ];

		syms X xi_ yi_ zi_ real;
		xi_ = h_range * cos(h_yaw) * cos(h_pitch);
		yi_ = h_range * sin(h_yaw) * cos(h_pitch);
		zi_ = -h_range * sin(h_pitch);

		X=[xi_ yi_ zi_];
		jacob_inv_mod=jacobian(X,H)
	  ------------------------------------------- */

	// The new point, relative to the sensor:
	const TPoint3D yn_rel_sensor(
		hn_r * chn_y * chn_p, hn_r * shn_y * chn_p, -hn_r * shn_p);

	// The Jacobian of the 3D point in the coordinate system of the sensor:
	/*
		[ cos(h_pitch)*cos(h_yaw), -h_range*cos(h_pitch)*sin(h_yaw),
	   -h_range*cos(h_yaw)*sin(h_pitch)]
		[ cos(h_pitch)*sin(h_yaw),  h_range*cos(h_pitch)*cos(h_yaw),
	   -h_range*sin(h_pitch)*sin(h_yaw)]
		[           -sin(h_pitch),                                0,
	   -h_range*cos(h_pitch)]
	*/
	const kftype values_dynlocal_dhn[] = {
		chn_p * chn_y,
		-hn_r * chn_p * shn_y,
		-hn_r * chn_y * shn_p,
		chn_p * shn_y,
		hn_r * chn_p * chn_y,
		-hn_r * shn_p * shn_y,
		-shn_p,
		0,
		-hn_r * chn_p};
	const KFMatrix_FxO dynlocal_dhn(values_dynlocal_dhn);

	KFMatrix_FxF jacob_dyn_dynrelsensor(UNINITIALIZED_MATRIX);
	KFMatrix_FxV jacob_dyn_dsensorabs(UNINITIALIZED_MATRIX);

	sensorPoseAbs.composePoint(
		yn_rel_sensor.x, yn_rel_sensor.y,
		yn_rel_sensor.z,  // yn rel. to the sensor
		yn[0], yn[1], yn[2],  // yn in global coords
		&jacob_dyn_dynrelsensor, &jacob_dyn_dsensorabs);

	dyn_dhn = jacob_dyn_dynrelsensor * dynlocal_dhn;

	// dyn_dxv =
	dyn_dxv = jacob_dyn_dsensorabs * dsensorabs_dvehpose;

	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 CRangeBearingKFSLAM::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);
	}

	MRPT_END
}

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

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

	// 3D ellipsoid for robot pose:
	CPointPDFGaussian pointGauss;
	pointGauss.mean.x(m_xkk[0]);
	pointGauss.mean.y(m_xkk[1]);
	pointGauss.mean.z(m_xkk[2]);
	pointGauss.cov = m_pkk.blockCopy<3, 3>(0, 0);

	{
		auto ellip = opengl::CEllipsoid3D::Create();

		ellip->setPose(pointGauss.mean);
		ellip->setCovMatrix(pointGauss.cov);
		ellip->enableDrawSolid3D(false);
		ellip->setQuantiles(options.quantiles_3D_representation);
		ellip->set3DsegmentsCount(10);
		ellip->setColor(1, 0, 0);

		outObj->insert(ellip);
	}

	// 3D ellipsoids for landmarks:
	const size_t nLMs = this->getNumberOfLandmarksInTheMap();
	for (size_t i = 0; i < nLMs; i++)
	{
		pointGauss.mean.x(
			m_xkk[get_vehicle_size() + get_feature_size() * i + 0]);
		pointGauss.mean.y(
			m_xkk[get_vehicle_size() + get_feature_size() * i + 1]);
		pointGauss.mean.z(
			m_xkk[get_vehicle_size() + get_feature_size() * i + 2]);

		pointGauss.cov =
			m_pkk.blockCopy<get_feature_size(), get_feature_size()>(
				get_vehicle_size() + get_feature_size() * i,
				get_vehicle_size() + get_feature_size() * i);

		auto ellip = opengl::CEllipsoid3D::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->set3DsegmentsCount(10);

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

		// Set color depending on partitions?
		if (options.doPartitioningExperiment)
		{
			// This is done for each landmark:
			map<int, bool> belongToPartition;
			const CSimpleMap* SFs =
				&m_SFs;	 // mapPartitioner.getSequenceOfFrames();

			for (size_t p = 0; p < m_lastPartitionSet.size(); p++)
			{
				for (size_t w = 0; w < m_lastPartitionSet[p].size(); w++)
				{
					// Check if landmark #i is in the SF of
					// m_lastPartitionSet[p][w]:
					CLandmark::TLandmarkID i_th_ID = m_IDs.inverse(i);

					// Look for the lm_ID in the SF:
					const CSensoryFrame::ConstPtr& SF_i =
						SFs->getAsPair(m_lastPartitionSet[p][w]).sf;

					CObservationBearingRange::Ptr obs =
						SF_i->getObservationByClass<CObservationBearingRange>();

					for (auto& o : obs->sensedData)
					{
						if (o.landmarkID == i_th_ID)
						{
							belongToPartition[p] = true;
							break;
						}
					}  // end for o
				}  // end for w
			}  // end for p

			// Build label:
			string strParts("[");

			for (auto it = belongToPartition.begin();
				 it != belongToPartition.end(); ++it)
			{
				if (it != belongToPartition.begin()) strParts += string(",");
				strParts += format("%i", it->first);
			}

			ellip->setName(ellip->getName() + strParts + string("]"));
		}

		outObj->insert(ellip);
	}
}

/*---------------------------------------------------------------
						getLastPartitionLandmarks
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::getLastPartitionLandmarksAsIfFixedSubmaps(
	size_t K, std::vector<std::vector<uint32_t>>& landmarksMembership)
{
	// temporary copy:
	std::vector<std::vector<uint32_t>> tmpParts = m_lastPartitionSet;

	// Fake "m_lastPartitionSet":

	// Fixed partitions every K observations:
	vector<std::vector<uint32_t>> partitions;
	std::vector<uint32_t> tmpCluster;

	for (size_t i = 0; i < m_SFs.size(); i++)
	{
		tmpCluster.push_back(i);
		if ((i % K) == 0)
		{
			partitions.push_back(tmpCluster);
			tmpCluster.clear();
			tmpCluster.push_back(
				i);	 // This observation "i" is shared between both clusters
		}
	}
	m_lastPartitionSet = partitions;

	// Call the actual method:
	getLastPartitionLandmarks(landmarksMembership);

	// Replace copy:
	m_lastPartitionSet = tmpParts;	//-V519
}

/*---------------------------------------------------------------
						getLastPartitionLandmarks
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::getLastPartitionLandmarks(
	std::vector<std::vector<uint32_t>>& landmarksMembership) const
{
	landmarksMembership.clear();

	// All the computation is made based on "m_lastPartitionSet"

	if (!options.doPartitioningExperiment) return;

	const size_t nLMs = this->getNumberOfLandmarksInTheMap();
	for (size_t i = 0; i < nLMs; i++)
	{
		map<int, bool> belongToPartition;
		const CSimpleMap* SFs =
			&m_SFs;	 // mapPartitioner.getSequenceOfFrames();

		for (size_t p = 0; p < m_lastPartitionSet.size(); p++)
		{
			for (size_t w = 0; w < m_lastPartitionSet[p].size(); w++)
			{
				// Check if landmark #i is in the SF of
				// m_lastPartitionSet[p][w]:
				CLandmark::TLandmarkID i_th_ID = m_IDs.inverse(i);

				// Look for the lm_ID in the SF:
				const CSensoryFrame::ConstPtr& SF_i =
					SFs->getAsPair(m_lastPartitionSet[p][w]).sf;

				CObservationBearingRange::Ptr obs =
					SF_i->getObservationByClass<CObservationBearingRange>();

				for (auto& o : obs->sensedData)
				{
					if (o.landmarkID == i_th_ID)
					{
						belongToPartition[p] = true;
						break;
					}
				}  // end for o
			}  // end for w
		}  // end for p

		// Build membership list:
		std::vector<uint32_t> membershipOfThisLM;

		for (auto& it : belongToPartition)
			membershipOfThisLM.push_back(it.first);

		landmarksMembership.push_back(membershipOfThisLM);
	}  // end for i
}

/*---------------------------------------------------------------
			computeOffDiagonalBlocksApproximationError
  ---------------------------------------------------------------*/
double CRangeBearingKFSLAM::computeOffDiagonalBlocksApproximationError(
	const std::vector<std::vector<uint32_t>>& landmarksMembership) const
{
	MRPT_START

	// Compute the information matrix:
	CMatrixDynamic<kftype> fullCov(m_pkk);
	size_t i;
	for (i = 0; i < get_vehicle_size(); i++)
		fullCov(i, i) = max(fullCov(i, i), 1e-6);

	CMatrixDynamic<kftype> H(fullCov.inverse_LLt());
	H.array().abs();  // Replace by absolute values:

	double sumOffBlocks = 0;
	unsigned int nLMs = landmarksMembership.size();

	ASSERT_(
		int(get_vehicle_size() + nLMs * get_feature_size()) == fullCov.cols());

	for (i = 0; i < nLMs; i++)
	{
		for (size_t j = i + 1; j < nLMs; j++)
		{
			// Sum the cross cov. between LM(i) and LM(j)??
			// --> Only if there is no common cluster:
			if (0 ==
				math::countCommonElements(
					landmarksMembership[i], landmarksMembership[j]))
			{
				size_t col = get_vehicle_size() + i * get_feature_size();
				size_t row = get_vehicle_size() + j * get_feature_size();
				sumOffBlocks += 2 * H.block<2, 2>(row, col).sum();
			}
		}
	}

	return sumOffBlocks /
		H.asEigen()
			.block(
				get_vehicle_size(), get_vehicle_size(),
				H.rows() - get_vehicle_size(),
				H.cols() - get_vehicle_size())
			.sum();	 // Starting (7,7)-end
	MRPT_END
}

/*---------------------------------------------------------------
			  reconsiderPartitionsNow
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::reconsiderPartitionsNow()
{
	// A different buffer for making this
	// thread-safe some day...
	vector<std::vector<uint32_t>> partitions;
	mapPartitioner.updatePartitions(partitions);
	m_lastPartitionSet = partitions;
}

/*---------------------------------------------------------------
			  saveMapAndPathRepresentationAsMATLABFile
  ---------------------------------------------------------------*/
void CRangeBearingKFSLAM::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,
		"%% "
		"'CRangeBearingKFSLAM::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");

	const size_t nLMs = this->getNumberOfLandmarksInTheMap();

	for (size_t 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 (size_t i = 0; i < m_SFs.size(); i++)
		{
			CPose3D p = m_SFs.getAsPair(i).pose->getMeanVal();

			os::fprintf(f, "%.04f %.04f", p.x(), p.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 CRangeBearingKFSLAM::OnSubstractObservationVectors(
	KFArray_OBS& A, const KFArray_OBS& B) const
{
	A -= B;
	mrpt::math::wrapToPiInPlace(A[1]);
	mrpt::math::wrapToPiInPlace(A[2]);
}

/** 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 CRangeBearingKFSLAM::OnGetObservationNoise(KFMatrix_OxO& out_R) const
{
	out_R(0, 0) = square(options.std_sensor_range);
	out_R(1, 1) = square(options.std_sensor_yaw);
	out_R(2, 2) = square(options.std_sensor_pitch);
}

/** 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 CRangeBearingKFSLAM::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");

#define USE_HEURISTIC_PREDICTION

#ifdef USE_HEURISTIC_PREDICTION
	const double sensor_max_range = obs->maxSensorDistance;
	const double fov_yaw = obs->fieldOfView_yaw;
	const double fov_pitch = obs->fieldOfView_pitch;

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

	out_LM_indices_to_predict.clear();
	for (size_t i = 0; i < prediction_means.size(); i++)
	{
#ifndef USE_HEURISTIC_PREDICTION
		out_LM_indices_to_predict.push_back(i);
#else
		// Heuristic: faster but doesn't work always!
		if (prediction_means[i][0] <
				(15 + sensor_max_range + max_vehicle_loc_uncertainty +
				 4 * options.std_sensor_range) &&
			fabs(prediction_means[i][1]) <
				(30.0_deg + 0.5 * fov_yaw + 4 * options.std_sensor_yaw) &&
			fabs(prediction_means[i][2]) <
				(30.0_deg + 0.5 * fov_pitch + 4 * options.std_sensor_pitch))
		{ out_LM_indices_to_predict.push_back(i); }
#endif
	}
}
