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207 | #ifndef MULTI_KALMAN_OBSTACLE_LOCATOR_H
#define MULTI_KALMAN_OBSTACLE_LOCATOR_H
#include <ModuleFramework/Module.h>
#include <Eigen/StdVector> //necessary for alignement in std::vector to work
// representations
#include "Representations/Perception/MultiBallPercept.h"
#include "Representations/Modeling/BallModel.h"
#include "Representations/Modeling/BodyState.h"
#include "Representations/Modeling/PlayerInfo.h"
#include "Representations/Modeling/OdometryData.h"
#include "Representations/Modeling/KinematicChain.h"
#include "Representations/Infrastructure/FrameInfo.h"
#include "Representations/Motion/MotionStatus.h"
#include "Representations/Perception/CameraMatrix.h"
#include "Representations/Infrastructure/FieldInfo.h"
// tools
#include "ObstacleHypothesis.h"
#include "UpdateAssociationFunctions.h"
// debug
#include "Tools/Debug/DebugDrawings.h"
#include "Tools/Debug/DebugRequest.h"
#include "Tools/Debug/DebugParameterList.h"
#include "Tools/Debug/DebugPlot.h"
#include "Tools/Debug/Color.h"
//////////////////// BEGIN MODULE INTERFACE DECLARATION ////////////////////
BEGIN_DECLARE_MODULE(MultiKalmanObstacleLocator)
// debug stuff
PROVIDE(DebugDrawings)
PROVIDE(DebugRequest)
PROVIDE(DebugParameterList)
PROVIDE(DebugPlot)
REQUIRE(FieldInfo)
REQUIRE(BodyState)
REQUIRE(PlayerInfo)
REQUIRE(FrameInfo)
REQUIRE(OdometryData)
// for preview stuff
REQUIRE(MotionStatus)
REQUIRE(KinematicChain)
REQUIRE(MultiBallPercept)
// for extended kalman filter
REQUIRE(CameraInfo)
REQUIRE(CameraInfoTop)
REQUIRE(CameraMatrix)
REQUIRE(CameraMatrixTop)
PROVIDE(BallModel)
END_DECLARE_MODULE(MultiKalmanObstacleLocator)
//////////////////// END MODULE INTERFACE DECLARATION //////////////////////
class MultiKalmanObstacleLocator : private MultiKalmanObstacleLocatorBase
{
public:
MultiKalmanObstacleLocator();
virtual ~MultiKalmanObstacleLocator();
virtual void execute();
// from other kalman filter ball locator
private:
OdometryData lastRobotOdometry;
FrameInfo lastFrameInfo;
private:
typedef std::vector<ObstacleHypothesis, Eigen::aligned_allocator<ObstacleHypothesis> > Filters;
Filters filter;
Filters::const_iterator bestModel;
// TODO: does it make sense to use the numerical epsilon: std::numeric_limits<double>::epsilon() or std::numeric_limits<float>::epsilon()?
// TODO: or is this value specific to the algorithms? E.g. ball speed below 1mm/s is considered 0.
const double epsilon=10e-6;
public:
const Filters& get_filter() {
return filter;
}
void clear_filter() {
filter.clear();
}
private:
void updateByPerceptsCool();
void updateByPerceptsNormal();
void updateByPerceptsGreedy(CameraInfo::CameraID camera);
void applyOdometryOnFilterState(ExtendedKalmanFilter4d& filter);
void predict(ExtendedKalmanFilter4d& filter, double dt) const;
Filters::const_iterator selectBestModel() const;
Filters::const_iterator selectBestModelBasedOnCovariance() const;
void provideBallModel(const ObstacleHypothesis &model);
/* DEBUG STUFF */
void doDebugRequest();
void doDebugRequestBeforPredictionAndUpdate();
void doDebugRequestBeforUpdate();
void drawFilter(const ObstacleHypothesis& bh, const Color& model_color, Color cov_loc_color, Color cov_vel_color) const;
void drawFiltersOnField() const;
void reloadParameters();
class Parameters: public ParameterList
{
inline void enforceSymmetryOfQ(double v){
processNoiseStdSingleDimension(0,1) = v;
}
inline void enforceSymmetryOfR(double v){
measurementNoiseCovariances(0,1) = v;
}
inline void enforceSymmetryOfInitialP(double v){
initialStateStdSingleDimension(0,1) = v;
}
public:
Parameters() : ParameterList("MultiKalmanObstacleLocator")<--- Member variable 'Parameters::area95Threshold' is not initialized in the constructor.
{
registerParameter("processNoiseStdQ00", processNoiseStdSingleDimension(0,0)) = 15;
registerParameter("processNoiseStdQ10", processNoiseStdSingleDimension(1,0), &Parameters::enforceSymmetryOfQ) = 0;
registerParameter("processNoiseStdQ11", processNoiseStdSingleDimension(1,1)) = 500;
// experimental determined
registerParameter("measurementNoiseR00", measurementNoiseCovariances(0,0)) = 0.00130217; //[rad^2]
registerParameter("measurementNoiseR10", measurementNoiseCovariances(1,0), &Parameters::enforceSymmetryOfR) = -0.00041764; //[rad^2]
registerParameter("measurementNoiseR11", measurementNoiseCovariances(1,1)) = 0.00123935; //[rad^2]
registerParameter("initialStateStdP00", initialStateStdSingleDimension(0,0)) = 250;
registerParameter("initialStateStdP10", initialStateStdSingleDimension(1,0), &Parameters::enforceSymmetryOfInitialP) = 0;
registerParameter("initialStateStdP11", initialStateStdSingleDimension(1,1)) = 250;
//thresholds for association functions
PARAMETER_REGISTER(euclidThreshold) = Math::fromDegrees(10);
PARAMETER_REGISTER(mahalanobisThreshold) = Math::fromDegrees(10);
PARAMETER_REGISTER(maximumLikelihoodThreshold) = 0.0005;
//AssymetricalBoolFilte parameters
PARAMETER_REGISTER(g0) = 0.03;
PARAMETER_REGISTER(g1) = 0.5;
PARAMETER_REGISTER(association.use_normal) = false;
PARAMETER_REGISTER(association.use_cool) = false;
PARAMETER_REGISTER(association.use_greedy) = true;
PARAMETER_REGISTER(area95Threshold_radius.factor) = std::sqrt(2) * 1;
PARAMETER_REGISTER(area95Threshold_radius.offset) = std::sqrt(2) * 125;
PARAMETER_REGISTER(use_covariance_based_selection) = true;
syncWithConfig();
}
double g0;
double g1;
Eigen::Matrix2d processNoiseStdSingleDimension;
Eigen::Matrix2d measurementNoiseCovariances;
Eigen::Matrix2d initialStateStdSingleDimension;
double area95Threshold;
double euclidThreshold;
double mahalanobisThreshold;
double maximumLikelihoodThreshold;
struct {
bool use_normal;
bool use_cool;
bool use_greedy;
} association;
struct {
double factor;
double offset;
} area95Threshold_radius;
bool use_covariance_based_selection;
} params;
Measurement_Function_H h;
UpdateAssociationFunction* updateAssociationFunction;
// available update association value functions
EuclideanUAF euclid;
MahalanobisUAF mahalanobis;
LikelihoodUAF likelihood;
};
#endif // MULTI_KALMAN_OBSTACLE_LOCATOR_H
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