Odometry.cpp

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/*
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.

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*/

#include <rtabmap/core/OdometryF2M.h>
#include "rtabmap/core/Odometry.h"
#include "rtabmap/core/OdometryF2F.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/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::kTypeF2F:
                odometry = new OdometryF2F(parameters);
                break;
        default:
                odometry = new OdometryF2M(parameters);
                type = Odometry::kTypeLocalMap;
                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()),
                _pose(Transform::getIdentity()),
                _resetCurrentCount(0),
                previousStamp_(0),
                distanceTravelled_(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);
        UASSERT(_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;
        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(!data.imageRaw().empty());

        // Ground alignment
        if(_pose.isIdentity() && _alignWithGround)
        {
                UTimer alignTimer;
                pcl::IndicesPtr indices(new std::vector<int>);
                pcl::IndicesPtr ground, obstacles;
                pcl::PointCloud<pcl::PointXYZ>::Ptr cloud = util3d::cloudFromSensorData(data, 1, 0, 0, indices.get());
                cloud = util3d::voxelize(cloud, indices, 0.01);
                bool success = false;
                if(cloud->size())
                {
                        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 \"Odom/AlignWithGround\" is true. "
                                        "Make sure the camera is seeing the ground (e.g., tilt ~30 "
                                        "degrees toward the ground).");
                }
        }

        if(!data.stereoCameraModel().isValidForProjection() &&
           (data.cameraModels().size() == 0 || !data.cameraModels()[0].isValidForProjection()))
        {
                UERROR("Rectified images required! Calibrate your camera.");
                return Transform();
        }

        double dt = previousStamp_>0.0f?data.stamp() - previousStamp_:0.0;
        Transform guess = dt && guessFromMotion_ && !previousVelocityTransform_.isNull()?Transform::getIdentity():Transform();
        UASSERT_MSG(dt>0.0 || (dt == 0.0 && previousVelocityTransform_.isNull()), uFormat("dt=%f previous transform=%s", dt, previousVelocityTransform_.prettyPrint().c_str()).c_str());
        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)
        {
                // 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.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(!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;
                        }
                }

                previousStamp_ = data.stamp();
                previousVelocityTransform_.setNull();
                if(dt)
                {
                        previousVelocityTransform_ = Transform(vx, vy, vz, vroll, vpitch, vyaw);
                }

                if(info)
                {
                        distanceTravelled_ += t.getNorm();
                        info->distanceTravelled = distanceTravelled_;
                }

                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 */