Odometry: Added Kalman Filtering option

This commit is contained in:
matlabbe
2015-12-14 16:50:17 -05:00
parent 82d76f3e99
commit f5c062448d
7 changed files with 366 additions and 21 deletions
+7 -1
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@@ -70,6 +70,9 @@ public:
private: private:
virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0) = 0; virtual Transform computeTransform(const SensorData & image, OdometryInfo * info = 0) = 0;
void initKalmanFilter();
void updateKalmanFilter(float dt, float & x, float & y, float & z, float & roll, float & pitch, float & yaw);
private: private:
std::string _roiRatios; std::string _roiRatios;
int _minInliers; int _minInliers;
@@ -80,7 +83,7 @@ private:
int _resetCountdown; int _resetCountdown;
bool _force2D; bool _force2D;
bool _holonomic; bool _holonomic;
bool _particleFiltering; int _filteringStrategy;
int _particleSize; int _particleSize;
float _particleNoiseT; float _particleNoiseT;
float _particleLambdaT; float _particleLambdaT;
@@ -91,6 +94,8 @@ private:
double _pnpReprojError; double _pnpReprojError;
int _pnpFlags; int _pnpFlags;
bool _varianceFromInliersCount; bool _varianceFromInliersCount;
float _kalmanProcessNoise;
float _kalmanMeasurementNoise;
Transform _pose; Transform _pose;
int _resetCurrentCount; int _resetCurrentCount;
double previousStamp_; double previousStamp_;
@@ -98,6 +103,7 @@ private:
float distanceTravelled_; float distanceTravelled_;
std::vector<ParticleFilter *> filters_; std::vector<ParticleFilter *> filters_;
cv::KalmanFilter kalmanFilter_;
protected: protected:
Odometry(const rtabmap::ParametersMap & parameters); Odometry(const rtabmap::ParametersMap & parameters);
+3 -1
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@@ -330,12 +330,14 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(Odom, Holonomic, bool, true, "If the robot is holonomic (strafing commands can be issued). If not, y value will be estimated from x and yaw values (y=x*tan(yaw))."); RTABMAP_PARAM(Odom, Holonomic, bool, true, "If the robot is holonomic (strafing commands can be issued). If not, y value will be estimated from x and yaw values (y=x*tan(yaw)).");
RTABMAP_PARAM(Odom, FillInfoData, bool, true, "Fill info with data (inliers/outliers features)."); RTABMAP_PARAM(Odom, FillInfoData, bool, true, "Fill info with data (inliers/outliers features).");
RTABMAP_PARAM(Odom, ImageBufferSize, unsigned int, 1, "Data buffer size (0 min inf)."); RTABMAP_PARAM(Odom, ImageBufferSize, unsigned int, 1, "Data buffer size (0 min inf).");
RTABMAP_PARAM(Odom, ParticleFiltering, bool, false, "Particle filtering to smooth the odometry trajectory."); RTABMAP_PARAM(Odom, FilteringStrategy, int, 0, "0=No filtering 1=Kalman filtering 2=Particle filtering");
RTABMAP_PARAM(Odom, ParticleSize, unsigned int, 400, "Number of particles of the filter."); RTABMAP_PARAM(Odom, ParticleSize, unsigned int, 400, "Number of particles of the filter.");
RTABMAP_PARAM(Odom, ParticleNoiseT, float, 0.002, "Noise (m) of translation components (x,y,z)."); RTABMAP_PARAM(Odom, ParticleNoiseT, float, 0.002, "Noise (m) of translation components (x,y,z).");
RTABMAP_PARAM(Odom, ParticleLambdaT, float, 100, "Lambda of translation components (x,y,z)."); RTABMAP_PARAM(Odom, ParticleLambdaT, float, 100, "Lambda of translation components (x,y,z).");
RTABMAP_PARAM(Odom, ParticleNoiseR, float, 0.002, "Noise (rad) of rotational components (roll,pitch,yaw)."); RTABMAP_PARAM(Odom, ParticleNoiseR, float, 0.002, "Noise (rad) of rotational components (roll,pitch,yaw).");
RTABMAP_PARAM(Odom, ParticleLambdaR, float, 100, "Lambda of rotational components (roll,pitch,yaw)."); RTABMAP_PARAM(Odom, ParticleLambdaR, float, 100, "Lambda of rotational components (roll,pitch,yaw).");
RTABMAP_PARAM(Odom, KalmanProcessNoise, float, 0.001, "Process noise covariance value.");
RTABMAP_PARAM(Odom, KalmanMeasurementNoise, float, 0.01, "Process measurement covariance value.");
// Odometry Bag-of-words // Odometry Bag-of-words
RTABMAP_PARAM(OdomBow, LocalHistorySize, int, 1000, "Local history size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words."); RTABMAP_PARAM(OdomBow, LocalHistorySize, int, 1000, "Local history size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words.");
+211 -7
View File
@@ -44,7 +44,7 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
_resetCountdown(Parameters::defaultOdomResetCountdown()), _resetCountdown(Parameters::defaultOdomResetCountdown()),
_force2D(Parameters::defaultVisForce2D()), _force2D(Parameters::defaultVisForce2D()),
_holonomic(Parameters::defaultOdomHolonomic()), _holonomic(Parameters::defaultOdomHolonomic()),
_particleFiltering(Parameters::defaultOdomParticleFiltering()), _filteringStrategy(Parameters::defaultOdomFilteringStrategy()),
_particleSize(Parameters::defaultOdomParticleSize()), _particleSize(Parameters::defaultOdomParticleSize()),
_particleNoiseT(Parameters::defaultOdomParticleNoiseT()), _particleNoiseT(Parameters::defaultOdomParticleNoiseT()),
_particleLambdaT(Parameters::defaultOdomParticleLambdaT()), _particleLambdaT(Parameters::defaultOdomParticleLambdaT()),
@@ -55,6 +55,8 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
_pnpReprojError(Parameters::defaultVisPnPReprojError()), _pnpReprojError(Parameters::defaultVisPnPReprojError()),
_pnpFlags(Parameters::defaultVisPnPFlags()), _pnpFlags(Parameters::defaultVisPnPFlags()),
_varianceFromInliersCount(Parameters::defaultRegVarianceFromInliersCount()), _varianceFromInliersCount(Parameters::defaultRegVarianceFromInliersCount()),
_kalmanProcessNoise(Parameters::defaultOdomKalmanProcessNoise()),
_kalmanMeasurementNoise(Parameters::defaultOdomKalmanMeasurementNoise()),
_resetCurrentCount(0), _resetCurrentCount(0),
previousStamp_(0), previousStamp_(0),
previousTransform_(Transform::getIdentity()), previousTransform_(Transform::getIdentity()),
@@ -76,7 +78,7 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
Parameters::parse(parameters, Parameters::kVisPnPFlags(), _pnpFlags); Parameters::parse(parameters, Parameters::kVisPnPFlags(), _pnpFlags);
UASSERT(_pnpFlags>=0 && _pnpFlags <=2); UASSERT(_pnpFlags>=0 && _pnpFlags <=2);
Parameters::parse(parameters, Parameters::kRegVarianceFromInliersCount(), _varianceFromInliersCount); Parameters::parse(parameters, Parameters::kRegVarianceFromInliersCount(), _varianceFromInliersCount);
Parameters::parse(parameters, Parameters::kOdomParticleFiltering(), _particleFiltering); Parameters::parse(parameters, Parameters::kOdomFilteringStrategy(), _filteringStrategy);
Parameters::parse(parameters, Parameters::kOdomParticleSize(), _particleSize); Parameters::parse(parameters, Parameters::kOdomParticleSize(), _particleSize);
Parameters::parse(parameters, Parameters::kOdomParticleNoiseT(), _particleNoiseT); Parameters::parse(parameters, Parameters::kOdomParticleNoiseT(), _particleNoiseT);
Parameters::parse(parameters, Parameters::kOdomParticleLambdaT(), _particleLambdaT); Parameters::parse(parameters, Parameters::kOdomParticleLambdaT(), _particleLambdaT);
@@ -86,8 +88,11 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
UASSERT(_particleLambdaT>0); UASSERT(_particleLambdaT>0);
UASSERT(_particleNoiseR>0); UASSERT(_particleNoiseR>0);
UASSERT(_particleLambdaR>0); UASSERT(_particleLambdaR>0);
if(_particleFiltering) Parameters::parse(parameters, Parameters::kOdomKalmanProcessNoise(), _kalmanProcessNoise);
Parameters::parse(parameters, Parameters::kOdomKalmanMeasurementNoise(), _kalmanMeasurementNoise);
if(_filteringStrategy == 2)
{ {
// Initialize the Particle filters
filters_.resize(6); filters_.resize(6);
for(unsigned int i = 0; i<filters_.size(); ++i) for(unsigned int i = 0; i<filters_.size(); ++i)
{ {
@@ -101,6 +106,10 @@ Odometry::Odometry(const rtabmap::ParametersMap & parameters) :
} }
} }
} }
else if(_filteringStrategy == 1)
{
initKalmanFilter();
}
} }
Odometry::~Odometry() Odometry::~Odometry()
@@ -150,6 +159,25 @@ void Odometry::reset(const Transform & initialPose)
filters_[4]->init(pitch); filters_[4]->init(pitch);
filters_[5]->init(yaw); filters_[5]->init(yaw);
} }
if(_filteringStrategy == 1)
{
if(_force2D)
{
kalmanFilter_.statePost.at<float>(0) = x;
kalmanFilter_.statePost.at<float>(1) = y;
kalmanFilter_.statePost.at<float>(6) = yaw;
}
else
{
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;
}
}
} }
else else
{ {
@@ -176,12 +204,13 @@ Transform Odometry::process(const SensorData & data, OdometryInfo * info)
UTimer time; UTimer time;
Transform t = this->computeTransform(data, info); Transform t = this->computeTransform(data, info);
double dt = data.stamp() - previousStamp_;
if(info) if(info)
{ {
info->timeEstimation = time.ticks(); info->timeEstimation = time.ticks();
info->lost = t.isNull(); info->lost = t.isNull();
info->stamp = data.stamp(); info->stamp = data.stamp();
info->interval = data.stamp() - previousStamp_; info->interval = dt;
info->transform = t; info->transform = t;
} }
@@ -192,13 +221,27 @@ Transform Odometry::process(const SensorData & data, OdometryInfo * info)
{ {
_resetCurrentCount = _resetCountdown; _resetCurrentCount = _resetCountdown;
if(_force2D || !_holonomic || filters_.size()) if(_force2D || !_holonomic || filters_.size() || _filteringStrategy==1)
{ {
float x,y,z, roll,pitch,yaw; float x,y,z, roll,pitch,yaw;
t.getTranslationAndEulerAngles(x, y, z, roll, pitch, yaw); t.getTranslationAndEulerAngles(x, y, z, roll, pitch, yaw);
if(filters_.size()) if(_filteringStrategy == 1)
{ {
if(_pose.isIdentity())
{
// reset Kalman
initKalmanFilter();
}
else
{
// Kalman filtering
updateKalmanFilter(dt,x,y,z,roll,pitch,yaw);
}
}
else if(filters_.size())
{
// Particle filtering
UASSERT(filters_.size()==6); UASSERT(filters_.size()==6);
if(_pose.isIdentity()) if(_pose.isIdentity())
{ {
@@ -261,7 +304,7 @@ Transform Odometry::process(const SensorData & data, OdometryInfo * info)
x, y, z, roll, pitch, yaw, t.prettyPrint().c_str()).c_str()); x, y, z, roll, pitch, yaw, t.prettyPrint().c_str()).c_str());
t = Transform(x,y,_force2D?0:z, _force2D?0:roll,_force2D?0:pitch,yaw); t = Transform(x,y,_force2D?0:z, _force2D?0:roll,_force2D?0:pitch,yaw);
if(info && filters_.size()) if(info && _filteringStrategy > 0)
{ {
info->transformFiltered = t; info->transformFiltered = t;
} }
@@ -296,4 +339,165 @@ Transform Odometry::process(const SensorData & data, OdometryInfo * info)
return Transform(); return Transform();
} }
void Odometry::initKalmanFilter()
{
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(_force2D)
{
nStates = 9; // the number of states (x,y,x',y',x'',y'',yaw,yaw',yaw'')
nMeasurements = 3; // the number of measured states (x,y,z,roll,pitch,yaw)
}
int nInputs = 0; // the number of action control
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
if(_force2D)
{
/* MEASUREMENT MODEL */
// [1 0 0 0 0 0 0 0 0]
// [0 1 0 0 0 0 0 0 0]
// [0 0 0 0 0 0 1 0 0]
kalmanFilter_.measurementMatrix.at<float>(0,0) = 1; // x
kalmanFilter_.measurementMatrix.at<float>(1,1) = 1; // y
kalmanFilter_.measurementMatrix.at<float>(2,6) = 1; // yaw
}
else
{
/* MEASUREMENT MODEL */
// [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 0 0 0]
kalmanFilter_.measurementMatrix.at<float>(0,0) = 1; // x
kalmanFilter_.measurementMatrix.at<float>(1,1) = 1; // y
kalmanFilter_.measurementMatrix.at<float>(2,2) = 1; // z
kalmanFilter_.measurementMatrix.at<float>(3,9) = 1; // roll
kalmanFilter_.measurementMatrix.at<float>(4,10) = 1; // pitch
kalmanFilter_.measurementMatrix.at<float>(5,11) = 1; // yaw
}
}
void Odometry::updateKalmanFilter(float dt, float & x, float & y, float & z, float & roll, float & pitch, float & yaw)
{
// Set transition matrix with current dt
if(_force2D)
{
// 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);
}
// Set measurement to predict
cv::Mat measurements;
if(!_force2D)
{
measurements = cv::Mat(6,1,CV_32FC1);
measurements.at<float>(0) = x; // x
measurements.at<float>(1) = y; // y
measurements.at<float>(2) = z; // z
measurements.at<float>(3) = roll; // roll
measurements.at<float>(4) = pitch; // pitch
measurements.at<float>(5) = yaw; // yaw
}
else
{
measurements = cv::Mat(3,1,CV_32FC1);
measurements.at<float>(0) = x; // x
measurements.at<float>(1) = y; // y
measurements.at<float>(5) = yaw; // yaw
}
// First predict, to update the internal statePre variable
UDEBUG("Predict");
cv::Mat prediction = kalmanFilter_.predict();
// The "correct" phase that is going to use the predicted value and our measurement
UDEBUG("Correct");
cv::Mat estimated = kalmanFilter_.correct(measurements);
if(_force2D)
{
x = estimated.at<float>(0);
y = estimated.at<float>(1);
yaw = estimated.at<float>(6);
}
else
{
x = estimated.at<float>(0);
y = estimated.at<float>(1);
z = estimated.at<float>(2);
roll = estimated.at<float>(9);
pitch = estimated.at<float>(10);
yaw = estimated.at<float>(11);
}
}
} /* namespace rtabmap */ } /* namespace rtabmap */
+2 -2
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@@ -204,7 +204,7 @@ Transform OdometryOpticalFlow::computeTransform(
std::vector<unsigned char> status; std::vector<unsigned char> status;
std::vector<float> err; std::vector<float> err;
UDEBUG("cv::calcOpticalFlowPyrLK() begin"); UDEBUG("cv::calcOpticalFlowPyrLK() begin");
int winSize = (newCorners.size()||!flowGuessFromMotion_)?flowWinSize_:(flowWinSize_*2); int winSize = flowWinSize_;
cv::calcOpticalFlowPyrLK( cv::calcOpticalFlowPyrLK(
refFrame_, refFrame_,
newLeftFrame, newLeftFrame,
@@ -213,7 +213,7 @@ Transform OdometryOpticalFlow::computeTransform(
status, status,
err, err,
cv::Size(winSize, winSize), cv::Size(winSize, winSize),
(newCorners.size()||!flowGuessFromMotion_)?flowMaxLevel_:flowMaxLevel_*2, (newCorners.size()||!flowGuessFromMotion_)?flowMaxLevel_:3,
cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, flowIterations_, flowEps_), cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, flowIterations_, flowEps_),
cv::OPTFLOW_LK_GET_MIN_EIGENVALS | (newCorners.size()?cv::OPTFLOW_USE_INITIAL_FLOW:0), 1e-4); cv::OPTFLOW_LK_GET_MIN_EIGENVALS | (newCorners.size()?cv::OPTFLOW_USE_INITIAL_FLOW:0), 1e-4);
UDEBUG("cv::calcOpticalFlowPyrLK() end"); UDEBUG("cv::calcOpticalFlowPyrLK() end");
+1
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@@ -145,6 +145,7 @@ const std::map<std::string, std::pair<bool, std::string> > & Parameters::getRemo
removedParameters_.insert(std::make_pair("RGBD/PoseScanMatching", std::make_pair(true, Parameters::kRGBDNeighborLinkRefining()))); removedParameters_.insert(std::make_pair("RGBD/PoseScanMatching", std::make_pair(true, Parameters::kRGBDNeighborLinkRefining())));
removedParameters_.insert(std::make_pair("Odom/ParticleFiltering", std::make_pair(false, Parameters::kOdomFilteringStrategy())));
removedParameters_.insert(std::make_pair("Odom/FeatureType", std::make_pair(true, Parameters::kVisFeatureType()))); removedParameters_.insert(std::make_pair("Odom/FeatureType", std::make_pair(true, Parameters::kVisFeatureType())));
removedParameters_.insert(std::make_pair("Odom/EstimationType", std::make_pair(true, Parameters::kVisEstimationType()))); removedParameters_.insert(std::make_pair("Odom/EstimationType", std::make_pair(true, Parameters::kVisEstimationType())));
removedParameters_.insert(std::make_pair("Odom/MaxFeatures", std::make_pair(true, Parameters::kVisMaxFeatures()))); removedParameters_.insert(std::make_pair("Odom/MaxFeatures", std::make_pair(true, Parameters::kVisMaxFeatures())));
+5 -1
View File
@@ -687,13 +687,17 @@ PreferencesDialog::PreferencesDialog(QWidget * parent) :
_ui->doubleSpinBox_maxVariance->setObjectName(Parameters::kOdomMonoMaxVariance().c_str()); _ui->doubleSpinBox_maxVariance->setObjectName(Parameters::kOdomMonoMaxVariance().c_str());
//Odometry particle filter //Odometry particle filter
_ui->odom_particleFiltering->setObjectName(Parameters::kOdomParticleFiltering().c_str()); _ui->odom_filteringStrategy->setObjectName(Parameters::kOdomFilteringStrategy().c_str());
_ui->spinBox_particleSize->setObjectName(Parameters::kOdomParticleSize().c_str()); _ui->spinBox_particleSize->setObjectName(Parameters::kOdomParticleSize().c_str());
_ui->doubleSpinBox_particleNoiseT->setObjectName(Parameters::kOdomParticleNoiseT().c_str()); _ui->doubleSpinBox_particleNoiseT->setObjectName(Parameters::kOdomParticleNoiseT().c_str());
_ui->doubleSpinBox_particleLambdaT->setObjectName(Parameters::kOdomParticleLambdaT().c_str()); _ui->doubleSpinBox_particleLambdaT->setObjectName(Parameters::kOdomParticleLambdaT().c_str());
_ui->doubleSpinBox_particleNoiseR->setObjectName(Parameters::kOdomParticleNoiseR().c_str()); _ui->doubleSpinBox_particleNoiseR->setObjectName(Parameters::kOdomParticleNoiseR().c_str());
_ui->doubleSpinBox_particleLambdaR->setObjectName(Parameters::kOdomParticleLambdaR().c_str()); _ui->doubleSpinBox_particleLambdaR->setObjectName(Parameters::kOdomParticleLambdaR().c_str());
//Odometry Kalman filter
_ui->doubleSpinBox_kalmanProcessNoise->setObjectName(Parameters::kOdomKalmanProcessNoise().c_str());
_ui->doubleSpinBox_kalmanMeasurementNoise->setObjectName(Parameters::kOdomKalmanMeasurementNoise().c_str());
//Stereo //Stereo
_ui->stereo_winWidth->setObjectName(Parameters::kStereoWinWidth().c_str()); _ui->stereo_winWidth->setObjectName(Parameters::kStereoWinWidth().c_str());
_ui->stereo_winHeight->setObjectName(Parameters::kStereoWinHeight().c_str()); _ui->stereo_winHeight->setObjectName(Parameters::kStereoWinHeight().c_str());
+137 -9
View File
@@ -86,7 +86,7 @@
<enum>QFrame::Raised</enum> <enum>QFrame::Raised</enum>
</property> </property>
<property name="currentIndex"> <property name="currentIndex">
<number>8</number> <number>17</number>
</property> </property>
<widget class="QWidget" name="page_22"> <widget class="QWidget" name="page_22">
<layout class="QVBoxLayout" name="verticalLayout_29" stretch="0,1"> <layout class="QVBoxLayout" name="verticalLayout_29" stretch="0,1">
@@ -6575,7 +6575,7 @@ see Sqlite3 doc 'PRAGMA temp_store'.</string>
<item row="4" column="1"> <item row="4" column="1">
<widget class="QLabel" name="label_233"> <widget class="QLabel" name="label_233">
<property name="text"> <property name="text">
<string>Particle filtering to smooth the odometry trajectory. See &quot;Particle Filter&quot; panel for the related parameters.</string> <string>Pose estimation filtering strategy.</string>
</property> </property>
<property name="wordWrap"> <property name="wordWrap">
<bool>true</bool> <bool>true</bool>
@@ -6592,13 +6592,6 @@ see Sqlite3 doc 'PRAGMA temp_store'.</string>
</property> </property>
</widget> </widget>
</item> </item>
<item row="4" column="0">
<widget class="QCheckBox" name="odom_particleFiltering">
<property name="text">
<string/>
</property>
</widget>
</item>
<item row="0" column="1"> <item row="0" column="1">
<widget class="QLabel" name="label_103"> <widget class="QLabel" name="label_103">
<property name="text"> <property name="text">
@@ -6714,6 +6707,28 @@ see Sqlite3 doc 'PRAGMA temp_store'.</string>
</property> </property>
</widget> </widget>
</item> </item>
<item row="4" column="0">
<widget class="QComboBox" name="odom_filteringStrategy">
<property name="sizeAdjustPolicy">
<enum>QComboBox::AdjustToContents</enum>
</property>
<item>
<property name="text">
<string>No filtering</string>
</property>
</item>
<item>
<property name="text">
<string>Kalman filtering</string>
</property>
</item>
<item>
<property name="text">
<string>Particle filtering</string>
</property>
</item>
</widget>
</item>
</layout> </layout>
</item> </item>
<item> <item>
@@ -7446,6 +7461,119 @@ see Sqlite3 doc 'PRAGMA temp_store'.</string>
</item> </item>
</layout> </layout>
</widget> </widget>
<widget class="QWidget" name="page_52">
<layout class="QVBoxLayout" name="verticalLayout_84">
<item>
<widget class="QGroupBox" name="groupBox_odometryKalmanFilter2">
<property name="title">
<string>Kalman Filter</string>
</property>
<layout class="QVBoxLayout" name="verticalLayout_171">
<item>
<widget class="QLabel" name="label_673">
<property name="text">
<string>Parameters for the Kalman filter when used to smooth the odometry trajectory.</string>
</property>
<property name="wordWrap">
<bool>true</bool>
</property>
<property name="textInteractionFlags">
<set>Qt::LinksAccessibleByMouse|Qt::TextSelectableByMouse</set>
</property>
</widget>
</item>
<item>
<layout class="QGridLayout" name="gridLayout_168" columnstretch="0,1">
<item row="1" column="0">
<widget class="QDoubleSpinBox" name="doubleSpinBox_kalmanMeasurementNoise">
<property name="suffix">
<string/>
</property>
<property name="decimals">
<number>5</number>
</property>
<property name="minimum">
<double>0.000000000000000</double>
</property>
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.010000000000000</double>
</property>
<property name="value">
<double>0.010000000000000</double>
</property>
</widget>
</item>
<item row="0" column="0">
<widget class="QDoubleSpinBox" name="doubleSpinBox_kalmanProcessNoise">
<property name="suffix">
<string/>
</property>
<property name="decimals">
<number>5</number>
</property>
<property name="minimum">
<double>0.000000000000000</double>
</property>
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.001000000000000</double>
</property>
<property name="value">
<double>0.001000000000000</double>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QLabel" name="label_674">
<property name="text">
<string>Process noise.</string>
</property>
<property name="wordWrap">
<bool>true</bool>
</property>
<property name="textInteractionFlags">
<set>Qt::LinksAccessibleByMouse|Qt::TextSelectableByMouse</set>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QLabel" name="label_675">
<property name="text">
<string>Measurement noise.</string>
</property>
<property name="wordWrap">
<bool>true</bool>
</property>
<property name="textInteractionFlags">
<set>Qt::LinksAccessibleByMouse|Qt::TextSelectableByMouse</set>
</property>
</widget>
</item>
</layout>
</item>
</layout>
</widget>
</item>
<item>
<spacer name="verticalSpacer_43">
<property name="orientation">
<enum>Qt::Vertical</enum>
</property>
<property name="sizeHint" stdset="0">
<size>
<width>20</width>
<height>1953</height>
</size>
</property>
</spacer>
</item>
</layout>
</widget>
<widget class="QWidget" name="page_46"> <widget class="QWidget" name="page_46">
<layout class="QVBoxLayout" name="verticalLayout_81"> <layout class="QVBoxLayout" name="verticalLayout_81">
<item> <item>