text: /*
  Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
  All rights reserved.
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:
      * Redistributions of source code must retain the above copyright
        notice, this list of conditions and the following disclaimer.
      * Redistributions in binary form must reproduce the above copyright
        notice, this list of conditions and the following disclaimer in the
        documentation and/or other materials provided with the distribution.
      * Neither the name of the Universite de Sherbrooke nor the
        names of its contributors may be used to endorse or promote products
        derived from this software without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  */
  
  #include <rtabmap/core/OdometryF2M.h>
  #include "rtabmap/core/Odometry.h"
  #include "rtabmap/core/OdometryF2F.h"
  #include "rtabmap/core/OdometryFovis.h"
  #include "rtabmap/core/OdometryViso2.h"
  #include "rtabmap/core/OdometryDVO.h"
  #include "rtabmap/core/OdometryOkvis.h"
  #include "rtabmap/core/OdometryORBSLAM2.h"
  #include "rtabmap/core/OdometryInfo.h"
  #include "rtabmap/core/util3d.h"
  #include "rtabmap/core/util3d_mapping.h"
  #include "rtabmap/core/util3d_filtering.h"
  #include "rtabmap/utilite/ULogger.h"
  #include "rtabmap/utilite/UTimer.h"
  #include "rtabmap/utilite/UConversion.h"
  #include "rtabmap/utilite/UProcessInfo.h"
  #include "rtabmap/core/ParticleFilter.h"
  #include "rtabmap/core/util2d.h"
  
  #include <pcl/pcl_base.h>
  
  namespace rtabmap {
  
  Odometry * Odometry::create(const ParametersMap & parameters)
  {
  	int odomTypeInt = Parameters::defaultOdomStrategy();
  	Parameters::parse(parameters, Parameters::kOdomStrategy(), odomTypeInt);
  	Odometry::Type type = (Odometry::Type)odomTypeInt;
  	return create(type, parameters);
  }
  
  Odometry * Odometry::create(Odometry::Type & type, const ParametersMap & parameters)
  {
  	UDEBUG("type=%d", (int)type);
  	Odometry * odometry = 0;
  	switch(type)
  	{
  	case Odometry::kTypeORBSLAM2:
  		odometry = new OdometryORBSLAM2(parameters);
  		break;
  	case Odometry::kTypeDVO:
  		odometry = new OdometryDVO(parameters);
  		break;
  	case Odometry::kTypeViso2:
  		odometry = new OdometryViso2(parameters);
  		break;
  	case Odometry::kTypeFovis:
  		odometry = new OdometryFovis(parameters);
  		break;
  	case Odometry::kTypeF2F:
  		odometry = new OdometryF2F(parameters);
  		break;
  	case Odometry::kTypeOkvis:
  		odometry = new OdometryOkvis(parameters);
  		break;
  	default:
  		odometry = new OdometryF2M(parameters);
  		type = Odometry::kTypeF2M;
  		break;
  	}
  	return odometry;
  }
  
  Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
  		_resetCountdown(Parameters::defaultOdomResetCountdown()),
  		_force3DoF(Parameters::defaultRegForce3DoF()),
  		_holonomic(Parameters::defaultOdomHolonomic()),
  		guessFromMotion_(Parameters::defaultOdomGuessMotion()),
  		_filteringStrategy(Parameters::defaultOdomFilteringStrategy()),
  		_particleSize(Parameters::defaultOdomParticleSize()),
  		_particleNoiseT(Parameters::defaultOdomParticleNoiseT()),
  		_particleLambdaT(Parameters::defaultOdomParticleLambdaT()),
  		_particleNoiseR(Parameters::defaultOdomParticleNoiseR()),
  		_particleLambdaR(Parameters::defaultOdomParticleLambdaR()),
  		_fillInfoData(Parameters::defaultOdomFillInfoData()),
  		_kalmanProcessNoise(Parameters::defaultOdomKalmanProcessNoise()),
  		_kalmanMeasurementNoise(Parameters::defaultOdomKalmanMeasurementNoise()),
  		_imageDecimation(Parameters::defaultOdomImageDecimation()),
  		_alignWithGround(Parameters::defaultOdomAlignWithGround()),
  		_publishRAMUsage(Parameters::defaultRtabmapPublishRAMUsage()),
  		_imagesAlreadyRectified(Parameters::defaultRtabmapImagesAlreadyRectified()),
  		_pose(Transform::getIdentity()),
  		_resetCurrentCount(0),
  		previousStamp_(0),
  		distanceTravelled_(0),
  		framesProcessed_(0)
  {
  	Parameters::parse(parameters, Parameters::kOdomResetCountdown(), _resetCountdown);
  
  	Parameters::parse(parameters, Parameters::kRegForce3DoF(), _force3DoF);
  	Parameters::parse(parameters, Parameters::kOdomHolonomic(), _holonomic);
  	Parameters::parse(parameters, Parameters::kOdomGuessMotion(), guessFromMotion_);
  	Parameters::parse(parameters, Parameters::kOdomFillInfoData(), _fillInfoData);
  	Parameters::parse(parameters, Parameters::kOdomFilteringStrategy(), _filteringStrategy);
  	Parameters::parse(parameters, Parameters::kOdomParticleSize(), _particleSize);
  	Parameters::parse(parameters, Parameters::kOdomParticleNoiseT(), _particleNoiseT);
  	Parameters::parse(parameters, Parameters::kOdomParticleLambdaT(), _particleLambdaT);
  	Parameters::parse(parameters, Parameters::kOdomParticleNoiseR(), _particleNoiseR);
  	Parameters::parse(parameters, Parameters::kOdomParticleLambdaR(), _particleLambdaR);
  	UASSERT(_particleNoiseT>0);
  	UASSERT(_particleLambdaT>0);
  	UASSERT(_particleNoiseR>0);
  	UASSERT(_particleLambdaR>0);
  	Parameters::parse(parameters, Parameters::kOdomKalmanProcessNoise(), _kalmanProcessNoise);
  	Parameters::parse(parameters, Parameters::kOdomKalmanMeasurementNoise(), _kalmanMeasurementNoise);
  	Parameters::parse(parameters, Parameters::kOdomImageDecimation(), _imageDecimation);
  	Parameters::parse(parameters, Parameters::kOdomAlignWithGround(), _alignWithGround);
  	Parameters::parse(parameters, Parameters::kRtabmapPublishRAMUsage(), _publishRAMUsage);
  	Parameters::parse(parameters, Parameters::kRtabmapImagesAlreadyRectified(), _imagesAlreadyRectified);
  
  	if(_imageDecimation == 0)
  	{
  		_imageDecimation = 1;
  	}
  
  	if(_filteringStrategy == 2)
  	{
  		// Initialize the Particle filters
  		particleFilters_.resize(6);
  		for(unsigned int i = 0; i<particleFilters_.size(); ++i)
  		{
  			if(i<3)
  			{
  				particleFilters_[i] = new ParticleFilter(_particleSize, _particleNoiseT, _particleLambdaT);
  			}
  			else
  			{
  				particleFilters_[i] = new ParticleFilter(_particleSize, _particleNoiseR, _particleLambdaR);
  			}
  		}
  	}
  	else if(_filteringStrategy == 1)
  	{
  		initKalmanFilter();
  	}
  }
  
  Odometry::~Odometry()
  {
  	for(unsigned int i=0; i<particleFilters_.size(); ++i)
  	{
  		delete particleFilters_[i];
  	}
  	particleFilters_.clear();
  }
  
  void Odometry::reset(const Transform & initialPose)
  {
  	UASSERT(!initialPose.isNull());
  	previousVelocityTransform_.setNull();
  	previousGroundTruthPose_.setNull();
  	_resetCurrentCount = 0;
  	previousStamp_ = 0;
  	distanceTravelled_ = 0;
  	framesProcessed_ = 0;
  	if(_force3DoF || particleFilters_.size())
  	{
  		float x,y,z, roll,pitch,yaw;
  		initialPose.getTranslationAndEulerAngles(x, y, z, roll, pitch, yaw);
  
  		if(_force3DoF)
  		{
  			if(z != 0.0f || roll != 0.0f || pitch != 0.0f)
  			{
  				UWARN("Force2D=true and the initial pose contains z, roll or pitch values (%s). They are set to null.", initialPose.prettyPrint().c_str());
  			}
  			z = 0;
  			roll = 0;
  			pitch = 0;
  			Transform pose(x, y, z, roll, pitch, yaw);
  			_pose = pose;
  		}
  		else
  		{
  			_pose = initialPose;
  		}
  
  		if(particleFilters_.size())
  		{
  			UASSERT(particleFilters_.size() == 6);
  			particleFilters_[0]->init(x);
  			particleFilters_[1]->init(y);
  			particleFilters_[2]->init(z);
  			particleFilters_[3]->init(roll);
  			particleFilters_[4]->init(pitch);
  			particleFilters_[5]->init(yaw);
  		}
  
  		if(_filteringStrategy == 1)
  		{
  			initKalmanFilter(initialPose);
  		}
  	}
  	else
  	{
  		_pose = initialPose;
  	}
  }
  
  Transform Odometry::process(SensorData & data, OdometryInfo * info)
  {
  	return process(data, Transform(), info);
  }
  
  Transform Odometry::process(SensorData & data, const Transform & guessIn, OdometryInfo * info)
  {
  	UASSERT_MSG(data.id() >= 0, uFormat("Input data should have ID greater or equal than 0 (id=%d)!", data.id()).c_str());
  
  	if(!_imagesAlreadyRectified && !this->canProcessRawImages())
  	{
  		UERROR("Odometry approach chosen cannot process raw images (not rectified images). Make sure images "
  				"are rectified, and set %s parameter back to true.",
  				Parameters::kRtabmapImagesAlreadyRectified().c_str());
  	}
  
  	// Ground alignment
  	if(_pose.isIdentity() && _alignWithGround)
  	{
  		if(data.depthOrRightRaw().empty())
  		{
  			UWARN("\"%s\" is true but the input has no depth information, ignoring alignment with ground...", Parameters::kOdomAlignWithGround().c_str());
  		}
  		else
  		{
  			UTimer alignTimer;
  			pcl::IndicesPtr indices(new std::vector<int>);
  			pcl::IndicesPtr ground, obstacles;
  			pcl::PointCloud<pcl::PointXYZ>::Ptr cloud = util3d::cloudFromSensorData(data, 1, 10, 0, indices.get());
  			bool success = false;
  			if(indices->size())
  			{
  				cloud = util3d::voxelize(cloud, indices, 0.01);
  				util3d::segmentObstaclesFromGround<pcl::PointXYZ>(cloud, ground, obstacles, 20, M_PI/4.0f, 0.02, 200, true);
  				if(ground->size())
  				{
  					pcl::ModelCoefficients coefficients;
  					util3d::extractPlane(cloud, ground, 0.02, 100, &coefficients);
  					if(coefficients.values.at(3) >= 0)
  					{
  						UWARN("Ground detected! coefficients=(%f, %f, %f, %f) time=%fs",
  								coefficients.values.at(0),
  								coefficients.values.at(1),
  								coefficients.values.at(2),
  								coefficients.values.at(3),
  								alignTimer.ticks());
  					}
  					else
  					{
  						UWARN("Ceiling detected! coefficients=(%f, %f, %f, %f) time=%fs",
  								coefficients.values.at(0),
  								coefficients.values.at(1),
  								coefficients.values.at(2),
  								coefficients.values.at(3),
  								alignTimer.ticks());
  					}
  					Eigen::Vector3f n(coefficients.values.at(0), coefficients.values.at(1), coefficients.values.at(2));
  					Eigen::Vector3f z(0,0,1);
  					//get rotation from z to n;
  					Eigen::Matrix3f R;
  					R = Eigen::Quaternionf().setFromTwoVectors(n,z);
  					Transform rotation(
  							R(0,0), R(0,1), R(0,2), 0,
  							R(1,0), R(1,1), R(1,2), 0,
  							R(2,0), R(2,1), R(2,2), coefficients.values.at(3));
  					_pose *= rotation;
  					success = true;
  				}
  			}
  			if(!success)
  			{
  				UERROR("Odometry failed to detect the ground. You have this "
  						"error because parameter \"%s\" is true. "
  						"Make sure the camera is seeing the ground (e.g., tilt ~30 "
  						"degrees toward the ground).", Parameters::kOdomAlignWithGround().c_str());
  			}
  		}
  	}
  
  	// KITTI datasets start with stamp=0
  	double dt = previousStamp_>0.0f || (previousStamp_==0.0f && framesProcessed()==1)?data.stamp() - previousStamp_:0.0;
  	Transform guess = dt>0.0 && guessFromMotion_ && !previousVelocityTransform_.isNull()?Transform::getIdentity():Transform();
  	if(!(dt>0.0 || (dt == 0.0 && previousVelocityTransform_.isNull())))
  	{
  		if(guessFromMotion_)
  		{
  			UERROR("Guess from motion is set but dt is invalid! Odometry is then computed without guess. (dt=%f previous transform=%s)", dt, previousVelocityTransform_.prettyPrint().c_str());
  		}
  		else if(_filteringStrategy==1)
  		{
  			UERROR("Kalman filtering is enalbed but dt is invalid! Odometry is then computed without Kalman filtering. (dt=%f previous transform=%s)", dt, previousVelocityTransform_.prettyPrint().c_str());
  		}
  		dt=0;
  		previousVelocityTransform_.setNull();
  	}
  	if(!previousVelocityTransform_.isNull())
  	{
  		if(guessFromMotion_)
  		{
  			if(_filteringStrategy == 1)
  			{
  				// use Kalman predict transform
  				float vx,vy,vz, vroll,vpitch,vyaw;
  				predictKalmanFilter(dt, &vx,&vy,&vz,&vroll,&vpitch,&vyaw);
  				guess = Transform(vx*dt, vy*dt, vz*dt, vroll*dt, vpitch*dt, vyaw*dt);
  			}
  			else
  			{
  				float vx,vy,vz, vroll,vpitch,vyaw;
  				previousVelocityTransform_.getTranslationAndEulerAngles(vx,vy,vz, vroll,vpitch,vyaw);
  				guess = Transform(vx*dt, vy*dt, vz*dt, vroll*dt, vpitch*dt, vyaw*dt);
  			}
  		}
  		else if(_filteringStrategy == 1)
  		{
  			predictKalmanFilter(dt);
  		}
  	}
  
  	if(!guessIn.isNull())
  	{
  		guess = guessIn;
  	}
  
  	UTimer time;
  	Transform t;
  	if(_imageDecimation > 1 && !data.imageRaw().empty())
  	{
  		// Decimation of images with calibrations
  		SensorData decimatedData = data;
  		decimatedData.setImageRaw(util2d::decimate(decimatedData.imageRaw(), _imageDecimation));
  		decimatedData.setDepthOrRightRaw(util2d::decimate(decimatedData.depthOrRightRaw(), _imageDecimation));
  		std::vector<CameraModel> cameraModels = decimatedData.cameraModels();
  		for(unsigned int i=0; i<cameraModels.size(); ++i)
  		{
  			cameraModels[i] = cameraModels[i].scaled(1.0/double(_imageDecimation));
  		}
  		decimatedData.setCameraModels(cameraModels);
  		StereoCameraModel stereoModel = decimatedData.stereoCameraModel();
  		if(stereoModel.isValidForProjection())
  		{
  			stereoModel.scale(1.0/double(_imageDecimation));
  		}
  		decimatedData.setStereoCameraModel(stereoModel);
  
  		// compute transform
  		t = this->computeTransform(decimatedData, guess, info);
  
  		// transform back the keypoints in the original image
  		std::vector<cv::KeyPoint> kpts = decimatedData.keypoints();
  		double log2value = log(double(_imageDecimation))/log(2.0);
  		for(unsigned int i=0; i<kpts.size(); ++i)
  		{
  			kpts[i].pt.x *= _imageDecimation;
  			kpts[i].pt.y *= _imageDecimation;
  			kpts[i].size *= _imageDecimation;
  			kpts[i].octave += log2value;
  		}
  		data.setFeatures(kpts, decimatedData.keypoints3D(), decimatedData.descriptors());
  
  		if(info)
  		{
  			UASSERT(info->newCorners.size() == info->refCorners.size());
  			for(unsigned int i=0; i<info->newCorners.size(); ++i)
  			{
  				info->refCorners[i].x *= _imageDecimation;
  				info->refCorners[i].y *= _imageDecimation;
  				info->newCorners[i].x *= _imageDecimation;
  				info->newCorners[i].y *= _imageDecimation;
  			}
  			for(std::multimap<int, cv::KeyPoint>::iterator iter=info->words.begin(); iter!=info->words.end(); ++iter)
  			{
  				iter->second.pt.x *= _imageDecimation;
  				iter->second.pt.y *= _imageDecimation;
  				iter->second.size *= _imageDecimation;
  				iter->second.octave += log2value;
  			}
  		}
  	}
  	else
  	{
  		t = this->computeTransform(data, guess, info);
  	}
  
  	if(info)
  	{
  		info->timeEstimation = time.ticks();
  		info->lost = t.isNull();
  		info->stamp = data.stamp();
  		info->interval = dt;
  		info->transform = t;
  		if(_publishRAMUsage)
  		{
  			info->memoryUsage = UProcessInfo::getMemoryUsage()/(1024*1024);
  		}
  
  		if(!data.groundTruth().isNull())
  		{
  			if(!previousGroundTruthPose_.isNull())
  			{
  				info->transformGroundTruth = previousGroundTruthPose_.inverse() * data.groundTruth();
  			}
  			previousGroundTruthPose_ = data.groundTruth();
  		}
  	}
  
  	if(!t.isNull())
  	{
  		_resetCurrentCount = _resetCountdown;
  
  		float vx,vy,vz, vroll,vpitch,vyaw;
  		t.getTranslationAndEulerAngles(vx,vy,vz, vroll,vpitch,vyaw);
  
  		// transform to velocity
  		if(dt)
  		{
  			vx /= dt;
  			vy /= dt;
  			vz /= dt;
  			vroll /= dt;
  			vpitch /= dt;
  			vyaw /= dt;
  		}
  
  		if(_force3DoF || !_holonomic || particleFilters_.size() || _filteringStrategy==1)
  		{
  			if(_filteringStrategy == 1)
  			{
  				if(previousVelocityTransform_.isNull())
  				{
  					// reset Kalman
  					if(dt)
  					{
  						initKalmanFilter(t, vx,vy,vz,vroll,vpitch,vyaw);
  					}
  					else
  					{
  						initKalmanFilter(t);
  					}
  				}
  				else
  				{
  					// Kalman filtering
  					updateKalmanFilter(vx,vy,vz,vroll,vpitch,vyaw);
  				}
  			}
  			else if(particleFilters_.size())
  			{
  				// Particle filtering
  				UASSERT(particleFilters_.size()==6);
  				if(previousVelocityTransform_.isNull())
  				{
  					particleFilters_[0]->init(vx);
  					particleFilters_[1]->init(vy);
  					particleFilters_[2]->init(vz);
  					particleFilters_[3]->init(vroll);
  					particleFilters_[4]->init(vpitch);
  					particleFilters_[5]->init(vyaw);
  				}
  				else
  				{
  					vx = particleFilters_[0]->filter(vx);
  					vy = particleFilters_[1]->filter(vy);
  					vyaw = particleFilters_[5]->filter(vyaw);
  
  					if(!_holonomic)
  					{
  						// arc trajectory around ICR
  						float tmpY = vyaw!=0.0f ? vx / tan((CV_PI-vyaw)/2.0f) : 0.0f;
  						if(fabs(tmpY) < fabs(vy) || (tmpY<=0 && vy >=0) || (tmpY>=0 && vy<=0))
  						{
  							vy = tmpY;
  						}
  						else
  						{
  							vyaw = (atan(vx/vy)*2.0f-CV_PI)*-1;
  						}
  					}
  
  					if(!_force3DoF)
  					{
  						vz = particleFilters_[2]->filter(vz);
  						vroll = particleFilters_[3]->filter(vroll);
  						vpitch = particleFilters_[4]->filter(vpitch);
  					}
  				}
  
  				if(info)
  				{
  					info->timeParticleFiltering = time.ticks();
  				}
  
  				if(_force3DoF)
  				{
  					vz = 0.0f;
  					vroll = 0.0f;
  					vpitch = 0.0f;
  				}
  			}
  			else if(!_holonomic)
  			{
  				// arc trajectory around ICR
  				vy = vyaw!=0.0f ? vx / tan((CV_PI-vyaw)/2.0f) : 0.0f;
  				if(_force3DoF)
  				{
  					vz = 0.0f;
  					vroll = 0.0f;
  					vpitch = 0.0f;
  				}
  			}
  
  			if(dt)
  			{
  				t = Transform(vx*dt, vy*dt, vz*dt, vroll*dt, vpitch*dt, vyaw*dt);
  			}
  			else
  			{
  				t = Transform(vx, vy, vz, vroll, vpitch, vyaw);
  			}
  
  			if(info)
  			{
  				info->transformFiltered = t;
  			}
  		}
  
  		if(data.stamp() == 0 && framesProcessed_ != 0)
  		{
  			UWARN("Null stamp detected");
  		}
  
  		previousStamp_ = data.stamp();
  		previousVelocityTransform_.setNull();
  
  		if(dt)
  		{
  			previousVelocityTransform_ = Transform(vx, vy, vz, vroll, vpitch, vyaw);
  		}
  
  		if(info)
  		{
  			distanceTravelled_ += t.getNorm();
  			info->distanceTravelled = distanceTravelled_;
  		}
  		++framesProcessed_;
  
  		return _pose *= t; // update
  	}
  	else if(_resetCurrentCount > 0)
  	{
  		UWARN("Odometry lost! Odometry will be reset after next %d consecutive unsuccessful odometry updates...", _resetCurrentCount);
  
  		--_resetCurrentCount;
  		if(_resetCurrentCount == 0)
  		{
  			UWARN("Odometry automatically reset to latest pose!");
  			this->reset(_pose);
  		}
  	}
  
  	previousVelocityTransform_.setNull();
  	previousStamp_ = 0;
  
  	return Transform();
  }
  
  void Odometry::initKalmanFilter(const Transform & initialPose, float vx, float vy, float vz, float vroll, float vpitch, float vyaw)
  {
  	UDEBUG("");
  	// See OpenCV tutorial: http://docs.opencv.org/master/dc/d2c/tutorial_real_time_pose.html
  	// See Kalman filter pose/orientation estimation theory: http://campar.in.tum.de/Chair/KalmanFilter
  
  	// initialize the Kalman filter
  	int nStates = 18;            // the number of states (x,y,z,x',y',z',x'',y'',z'',roll,pitch,yaw,roll',pitch',yaw',roll'',pitch'',yaw'')
  	int nMeasurements = 6;       // the number of measured states (x',y',z',roll',pitch',yaw')
  	if(_force3DoF)
  	{
  		nStates = 9;             // the number of states (x,y,x',y',x'',y'',yaw,yaw',yaw'')
  		nMeasurements = 3;       // the number of measured states (x',y',yaw')
  	}
  	int nInputs = 0;             // the number of action control
  
  	/* From viso2, measurement covariance
  	 * static const boost::array<double, 36> STANDARD_POSE_COVARIANCE =
  	{ { 0.1, 0, 0, 0, 0, 0,
  	    0, 0.1, 0, 0, 0, 0,
  	    0, 0, 0.1, 0, 0, 0,
  	    0, 0, 0, 0.17, 0, 0,
  	    0, 0, 0, 0, 0.17, 0,
  	    0, 0, 0, 0, 0, 0.17 } };
  	static const boost::array<double, 36> STANDARD_TWIST_COVARIANCE =
  	{ { 0.05, 0, 0, 0, 0, 0,
  	    0, 0.05, 0, 0, 0, 0,
  	    0, 0, 0.05, 0, 0, 0,
  	    0, 0, 0, 0.09, 0, 0,
  	    0, 0, 0, 0, 0.09, 0,
  	    0, 0, 0, 0, 0, 0.09 } };
  	 */
  
  
  	kalmanFilter_.init(nStates, nMeasurements, nInputs);                 // init Kalman Filter
  	cv::setIdentity(kalmanFilter_.processNoiseCov, cv::Scalar::all(_kalmanProcessNoise));  // set process noise
  	cv::setIdentity(kalmanFilter_.measurementNoiseCov, cv::Scalar::all(_kalmanMeasurementNoise));   // set measurement noise
  	cv::setIdentity(kalmanFilter_.errorCovPost, cv::Scalar::all(1));             // error covariance
  
  	float x,y,z,roll,pitch,yaw;
  	initialPose.getTranslationAndEulerAngles(x,y,z,roll,pitch,yaw);
  
  	if(_force3DoF)
  	{
          /* MEASUREMENT MODEL (velocity) */
  		//  [0 0 1 0 0 0 0 0 0]
  		//  [0 0 0 1 0 0 0 0 0]
  		//  [0 0 0 0 0 0 0 1 0]
  		kalmanFilter_.measurementMatrix.at<float>(0,2) = 1;  // x'
  		kalmanFilter_.measurementMatrix.at<float>(1,3) = 1;  // y'
  		kalmanFilter_.measurementMatrix.at<float>(2,7) = 1; // yaw'
  
  		kalmanFilter_.statePost.at<float>(0) = x;
  		kalmanFilter_.statePost.at<float>(1) = y;
  		kalmanFilter_.statePost.at<float>(6) = yaw;
  
  		kalmanFilter_.statePost.at<float>(2) = vx;
  		kalmanFilter_.statePost.at<float>(3) = vy;
  		kalmanFilter_.statePost.at<float>(7) = vyaw;
  	}
  	else
  	{
  	    /* MEASUREMENT MODEL (velocity) */
  		//  [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
  		//  [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
  		//  [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
  		//  [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
  		//  [0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]
  		//  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
  		kalmanFilter_.measurementMatrix.at<float>(0,3) = 1;  // x'
  		kalmanFilter_.measurementMatrix.at<float>(1,4) = 1;  // y'
  		kalmanFilter_.measurementMatrix.at<float>(2,5) = 1;  // z'
  		kalmanFilter_.measurementMatrix.at<float>(3,12) = 1; // roll'
  		kalmanFilter_.measurementMatrix.at<float>(4,13) = 1; // pitch'
  		kalmanFilter_.measurementMatrix.at<float>(5,14) = 1; // yaw'
  
  		kalmanFilter_.statePost.at<float>(0) = x;
  		kalmanFilter_.statePost.at<float>(1) = y;
  		kalmanFilter_.statePost.at<float>(2) = z;
  		kalmanFilter_.statePost.at<float>(9) = roll;
  		kalmanFilter_.statePost.at<float>(10) = pitch;
  		kalmanFilter_.statePost.at<float>(11) = yaw;
  
  		kalmanFilter_.statePost.at<float>(3) = vx;
  		kalmanFilter_.statePost.at<float>(4) = vy;
  		kalmanFilter_.statePost.at<float>(5) = vz;
  		kalmanFilter_.statePost.at<float>(12) = vroll;
  		kalmanFilter_.statePost.at<float>(13) = vpitch;
  		kalmanFilter_.statePost.at<float>(14) = vyaw;
  	}
  }
  
  void Odometry::predictKalmanFilter(float dt, float * vx, float * vy, float * vz, float * vroll, float * vpitch, float * vyaw)
  {
  	// Set transition matrix with current dt
  	if(_force3DoF)
  	{
  		// 2D:
  		//  [1 0 dt  0 dt2    0   0    0     0] x
  		//  [0 1  0 dt   0  dt2   0    0     0] y
  		//  [0 0  1  0   dt   0   0    0     0] x'
  		//  [0 0  0  1   0   dt   0    0     0] y'
  		//  [0 0  0  0   1    0   0    0     0] x''
  		//  [0 0  0  0   0    0   0    0     0] y''
  		//  [0 0  0  0   0    0   1   dt   dt2] yaw
  		//  [0 0  0  0   0    0   0    1    dt] yaw'
  		//  [0 0  0  0   0    0   0    0     1] yaw''
  		kalmanFilter_.transitionMatrix.at<float>(0,2) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(1,3) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(2,4) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(3,5) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(0,4) = 0.5*pow(dt,2);
  		kalmanFilter_.transitionMatrix.at<float>(1,5) = 0.5*pow(dt,2);
  		// orientation
  		kalmanFilter_.transitionMatrix.at<float>(6,7) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(7,8) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(6,8) = 0.5*pow(dt,2);
  	}
  	else
  	{
  		//  [1 0 0 dt  0  0 dt2   0   0 0 0 0  0  0  0   0   0   0] x
  		//  [0 1 0  0 dt  0   0 dt2   0 0 0 0  0  0  0   0   0   0] y
  		//  [0 0 1  0  0 dt   0   0 dt2 0 0 0  0  0  0   0   0   0] z
  		//  [0 0 0  1  0  0  dt   0   0 0 0 0  0  0  0   0   0   0] x'
  		//  [0 0 0  0  1  0   0  dt   0 0 0 0  0  0  0   0   0   0] y'
  		//  [0 0 0  0  0  1   0   0  dt 0 0 0  0  0  0   0   0   0] z'
  		//  [0 0 0  0  0  0   1   0   0 0 0 0  0  0  0   0   0   0] x''
  		//  [0 0 0  0  0  0   0   1   0 0 0 0  0  0  0   0   0   0] y''
  		//  [0 0 0  0  0  0   0   0   1 0 0 0  0  0  0   0   0   0] z''
  		//  [0 0 0  0  0  0   0   0   0 1 0 0 dt  0  0 dt2   0   0]
  		//  [0 0 0  0  0  0   0   0   0 0 1 0  0 dt  0   0 dt2   0]
  		//  [0 0 0  0  0  0   0   0   0 0 0 1  0  0 dt   0   0 dt2]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  1  0  0  dt   0   0]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  0  1  0   0  dt   0]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  0  0  1   0   0  dt]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  0  0  0   1   0   0]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  0  0  0   0   1   0]
  		//  [0 0 0  0  0  0   0   0   0 0 0 0  0  0  0   0   0   1]
  		// position
  		kalmanFilter_.transitionMatrix.at<float>(0,3) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(1,4) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(2,5) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(3,6) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(4,7) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(5,8) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(0,6) = 0.5*pow(dt,2);
  		kalmanFilter_.transitionMatrix.at<float>(1,7) = 0.5*pow(dt,2);
  		kalmanFilter_.transitionMatrix.at<float>(2,8) = 0.5*pow(dt,2);
  		// orientation
  		kalmanFilter_.transitionMatrix.at<float>(9,12) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(10,13) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(11,14) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(12,15) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(13,16) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(14,17) = dt;
  		kalmanFilter_.transitionMatrix.at<float>(9,15) = 0.5*pow(dt,2);
  		kalmanFilter_.transitionMatrix.at<float>(10,16) = 0.5*pow(dt,2);
  		kalmanFilter_.transitionMatrix.at<float>(11,17) = 0.5*pow(dt,2);
  	}
  
  	// First predict, to update the internal statePre variable
  	UDEBUG("Predict");
  	const cv::Mat & prediction = kalmanFilter_.predict();
  
  	if(vx)
  		*vx = prediction.at<float>(3);                      // x'
  	if(vy)
  		*vy = prediction.at<float>(4);                      // y'
  	if(vz)
  		*vz = _force3DoF?0.0f:prediction.at<float>(5);      // z'
  	if(vroll)
  		*vroll = _force3DoF?0.0f:prediction.at<float>(12);  // roll'
  	if(vpitch)
  		*vpitch = _force3DoF?0.0f:prediction.at<float>(13); // pitch'
  	if(vyaw)
  		*vyaw = prediction.at<float>(_force3DoF?7:14);      // yaw'
  }
  
  void Odometry::updateKalmanFilter(float & vx, float & vy, float & vz, float & vroll, float & vpitch, float & vyaw)
  {
  	// Set measurement to predict
  	cv::Mat measurements;
  	if(!_force3DoF)
  	{
  		measurements = cv::Mat(6,1,CV_32FC1);
  		measurements.at<float>(0) = vx;     // x'
  		measurements.at<float>(1) = vy;     // y'
  		measurements.at<float>(2) = vz;     // z'
  		measurements.at<float>(3) = vroll;  // roll'
  		measurements.at<float>(4) = vpitch; // pitch'
  		measurements.at<float>(5) = vyaw;   // yaw'
  	}
  	else
  	{
  		measurements = cv::Mat(3,1,CV_32FC1);
  		measurements.at<float>(0) = vx;     // x'
  		measurements.at<float>(1) = vy;     // y'
  		measurements.at<float>(2) = vyaw;   // yaw',
  	}
  
  	// The "correct" phase that is going to use the predicted value and our measurement
  	UDEBUG("Correct");
  	const cv::Mat & estimated = kalmanFilter_.correct(measurements);
  
  
  	vx = estimated.at<float>(3);                      // x'
  	vy = estimated.at<float>(4);                      // y'
  	vz = _force3DoF?0.0f:estimated.at<float>(5);      // z'
  	vroll = _force3DoF?0.0f:estimated.at<float>(12);  // roll'
  	vpitch = _force3DoF?0.0f:estimated.at<float>(13); // pitch'
  	vyaw = estimated.at<float>(_force3DoF?7:14);      // yaw'
  }
  
  } /* namespace rtabmap */
  