Visual Servoing Platform  version 3.2.0 under development (2018-12-14)
testKalmanVelocity.cpp

Test some vpLinearKalmanFilterInstantiation functionalities with constant velocity state model.

/****************************************************************************
*
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*
* Description:
* Tests some vpLinearKalmanFilterInstantiation functionalities.
*
* Authors:
* Fabien Spindler
*
*****************************************************************************/
#include <fstream>
#include <iostream>
#include <visp3/core/vpLinearKalmanFilterInstantiation.h>
typedef enum {
Position, // Considered measures are the succesive positions of the target
Velocity // Considered measures are the succesive velocities of the target
} vpMeasureType;
int main()
{
try {
unsigned int nsignal = 2; // Number of signal to filter
unsigned int niter = 200;
unsigned int size_state_vector = 2 * nsignal;
unsigned int size_measure_vector = 1 * nsignal;
// vpMeasureType measure_t = Velocity;
vpMeasureType measure_t = Position;
std::string filename = "/tmp/log.dat";
std::ofstream flog(filename.c_str());
vpColVector sigma_measure(size_measure_vector);
for (unsigned int signal = 0; signal < nsignal; signal++)
sigma_measure = 0.000001;
vpColVector sigma_state(size_state_vector);
switch (measure_t) {
case Velocity:
for (unsigned int signal = 0; signal < nsignal; signal++) {
sigma_state[2 * signal] = 0.; // not used
sigma_state[2 * signal + 1] = 0.000001;
}
break;
case Position:
for (unsigned int signal = 0; signal < nsignal; signal++) {
sigma_state[2 * signal] = 0.000001;
sigma_state[2 * signal + 1] = 0; // not used
}
break;
}
vpColVector measure(size_measure_vector);
for (unsigned int signal = 0; signal < nsignal; signal++) {
measure[signal] = 3 + 2 * signal;
}
kalman.verbose(true);
double dt = 0.04; // Sampling period
double rho = 0.5;
double dummy = 0; // non used parameter
switch (measure_t) {
case Velocity:
kalman.setStateModel(model);
kalman.initFilter(nsignal, sigma_state, sigma_measure, rho, dummy);
break;
case Position:
kalman.setStateModel(model);
kalman.initFilter(nsignal, sigma_state, sigma_measure, dummy, dt);
break;
}
for (unsigned int iter = 0; iter <= niter; iter++) {
std::cout << "-------- iter " << iter << " ------------" << std::endl;
for (unsigned int signal = 0; signal < nsignal; signal++) {
measure[signal] = 3 + 2 * signal + 0.3 * sin(vpMath::rad(360. / niter * iter));
}
std::cout << "measure : " << measure.t() << std::endl;
flog << measure.t();
// kalman.prediction();
kalman.filter(measure);
flog << kalman.Xest.t() << std::endl;
std::cout << "Xest: " << kalman.Xest.t() << std::endl;
}
flog.close();
return 0;
} catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
return 1;
}
}