Visual Servoing Platform  version 3.6.1 under development (2024-02-13)
vpLinearKalmanFilterInstantiation.cpp
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31  * Description:
32  * Kalman filtering.
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35 
41 #include <visp3/core/vpDebug.h>
42 #include <visp3/core/vpException.h>
43 #include <visp3/core/vpLinearKalmanFilterInstantiation.h>
44 
45 #include <math.h>
46 #include <stdlib.h>
47 
170 void vpLinearKalmanFilterInstantiation::initFilter(unsigned int n_signal, vpColVector &sigma_state,
171  vpColVector &sigma_measure, double rho, double delta_t)
172 {
173  switch (model) {
175  initStateConstVelWithColoredNoise_MeasureVel(n_signal, sigma_state, sigma_measure, rho);
176  break;
178  initStateConstVel_MeasurePos(n_signal, sigma_state, sigma_measure, delta_t);
179  break;
181  initStateConstAccWithColoredNoise_MeasureVel(n_signal, sigma_state, sigma_measure, rho, delta_t);
182  break;
183  case unknown:
184  vpERROR_TRACE("Kalman state model is not set");
185  throw(vpException(vpException::notInitialized, "Kalman state model is not set"));
186  }
187 }
188 
285  vpColVector &sigma_measure, double delta_t)
286 {
287  // init_done = true ;
289 
290  init(size_state, size_measure, n_signal);
291 
292  iter = 0;
293  Pest = 0;
294  Xest = 0;
295  F = 0;
296  H = 0;
297  R = 0;
298  Q = 0;
299  this->dt = delta_t;
300 
301  double dt2 = dt * dt;
302  double dt3 = dt2 * dt;
303 
304  for (unsigned int i = 0; i < size_measure * n_signal; i++) {
305  // State model
306  // | 1 dt |
307  // F = | |
308  // | 0 1 |
309 
310  F[2 * i][2 * i] = 1;
311  F[2 * i][2 * i + 1] = dt;
312  F[2 * i + 1][2 * i + 1] = 1;
313 
314  // Measure model
315  H[i][2 * i] = 1;
316  H[i][2 * i + 1] = 0;
317 
318  double sR = sigma_measure[i];
319  double sQ = sigma_state[2 * i]; // sigma_state[2*i+1] is not used
320 
321  // Measure noise
322  R[i][i] = sR;
323 
324  // State covariance matrix 6.2.2.12
325  Q[2 * i][2 * i] = sQ * dt3 / 3;
326  Q[2 * i][2 * i + 1] = sQ * dt2 / 2;
327  Q[2 * i + 1][2 * i] = sQ * dt2 / 2;
328  Q[2 * i + 1][2 * i + 1] = sQ * dt;
329 
330  Pest[2 * i][2 * i] = sR;
331  Pest[2 * i][2 * i + 1] = sR / (2 * dt);
332  Pest[2 * i + 1][2 * i] = sR / (2 * dt);
333  Pest[2 * i + 1][2 * i + 1] = sQ * 2 * dt / 3.0 + sR / (2 * dt2);
334  }
335 }
336 
491  vpColVector &sigma_state,
492  vpColVector &sigma_measure,
493  double rho)
494 {
495  if ((rho < 0) || (rho >= 1)) {
496  vpERROR_TRACE("Bad rho value %g; should be in [0:1[", rho);
497  throw(vpException(vpException::badValue, "Bad rho value"));
498  }
499 
501 
502  init(size_state, size_measure, n_signal);
503 
504  iter = 0;
505  Pest = 0;
506  Xest = 0;
507  F = 0;
508  H = 0;
509  R = 0;
510  Q = 0;
511 
512  for (unsigned int i = 0; i < size_measure * n_signal; i++) {
513  // State model
514  // | 1 1 |
515  // F = | |
516  // | 0 rho |
517 
518  F[2 * i][2 * i] = 1;
519  F[2 * i][2 * i + 1] = 1;
520  F[2 * i + 1][2 * i + 1] = rho;
521 
522  // Measure model
523  H[i][2 * i] = 1;
524  H[i][2 * i + 1] = 0;
525 
526  double sR = sigma_measure[i];
527  double sQ = sigma_state[2 * i + 1]; // sigma_state[2*i] is not used
528 
529  // Measure noise
530  R[i][i] = sR;
531 
532  // State covariance matrix
533  Q[2 * i][2 * i] = 0;
534  Q[2 * i][2 * i + 1] = 0;
535  Q[2 * i + 1][2 * i] = 0;
536  Q[2 * i + 1][2 * i + 1] = sQ;
537 
538  Pest[2 * i][2 * i] = sR;
539  Pest[2 * i][2 * i + 1] = 0.;
540  Pest[2 * i + 1][2 * i] = 0;
541  Pest[2 * i + 1][2 * i + 1] = sQ / (1 - rho * rho);
542  }
543 }
544 
711  vpColVector &sigma_state,
712  vpColVector &sigma_measure,
713  double rho, double delta_t)
714 {
715  if ((rho < 0) || (rho >= 1)) {
716  vpERROR_TRACE("Bad rho value %g; should be in [0:1[", rho);
717  throw(vpException(vpException::badValue, "Bad rho value"));
718  }
720 
721  init(size_state, size_measure, n_signal);
722 
723  iter = 0;
724  Pest = 0;
725  Xest = 0;
726  F = 0;
727  H = 0;
728  R = 0;
729  Q = 0;
730  this->dt = delta_t;
731  // initialise les matrices decrivant les modeles
732  for (unsigned int i = 0; i < size_measure * nsignal; i++) {
733  // State model
734  // | 1 1 dt |
735  // F = | o rho 0 |
736  // | 0 0 1 |
737 
738  F[3 * i][3 * i] = 1;
739  F[3 * i][3 * i + 1] = 1;
740  F[3 * i][3 * i + 2] = dt;
741  F[3 * i + 1][3 * i + 1] = rho;
742  F[3 * i + 2][3 * i + 2] = 1;
743 
744  // Measure model
745  H[i][3 * i] = 1;
746  H[i][3 * i + 1] = 0;
747  H[i][3 * i + 2] = 0;
748 
749  double sR = sigma_measure[i];
750  double sQ1 = sigma_state[3 * i + 1];
751  double sQ2 = sigma_state[3 * i + 2];
752 
753  // Measure noise
754  R[i][i] = sR;
755 
756  // State covariance matrix
757  Q[3 * i + 1][3 * i + 1] = sQ1;
758  Q[3 * i + 2][3 * i + 2] = sQ2;
759 
760  Pest[3 * i][3 * i] = sR;
761  Pest[3 * i][3 * i + 1] = 0.;
762  Pest[3 * i][3 * i + 2] = sR / dt;
763  Pest[3 * i + 1][3 * i + 1] = sQ1 / (1 - rho * rho);
764  Pest[3 * i + 1][3 * i + 2] = -rho * sQ1 / ((1 - rho * rho) * dt);
765  Pest[3 * i + 2][3 * i + 2] = (2 * sR + sQ1 / (1 - rho * rho)) / (dt * dt);
766  // complete the lower triangle
767  Pest[3 * i + 1][3 * i] = Pest[3 * i][3 * i + 1];
768  Pest[3 * i + 2][3 * i] = Pest[3 * i][3 * i + 2];
769  Pest[3 * i + 2][3 * i + 1] = Pest[3 * i + 1][3 * i + 2];
770  }
771 }
772 
787 {
788  if (nsignal < 1) {
789  vpERROR_TRACE("Bad signal number. You need to initialize the Kalman filter");
790  throw(vpException(vpException::notInitialized, "Bad signal number"));
791  }
792 
793  // Specific initialization of the filter that depends on the state model
794  if (iter == 0) {
795  Xest = 0;
796  switch (model) {
800  for (unsigned int i = 0; i < size_measure * nsignal; i++) {
801  Xest[size_state * i] = z[i];
802  }
803  prediction();
804  // init_done = true;
805  break;
806  case unknown:
807  vpERROR_TRACE("Kalman state model is not set");
808  throw(vpException(vpException::notInitialized, "Kalman state model is not set"));
809  }
810  iter++;
811 
812  return;
813  } else if (iter == 1) {
815  for (unsigned int i = 0; i < size_measure * nsignal; i++) {
816  double z_prev = Xest[size_state * i]; // Previous mesured position
817  Xest[size_state * i] = z[i];
818  Xest[size_state * i + 1] = (z[i] - z_prev) / dt;
819  }
820  prediction();
821  iter++;
822 
823  return;
824  }
825  }
826 
827  filtering(z);
828  prediction();
829 }
Implementation of column vector and the associated operations.
Definition: vpColVector.h:163
error that can be emitted by ViSP classes.
Definition: vpException.h:59
@ badValue
Used to indicate that a value is not in the allowed range.
Definition: vpException.h:85
@ notInitialized
Used to indicate that a parameter is not initialized.
Definition: vpException.h:86
unsigned int size_state
Size of the state vector .
long iter
Filter step or iteration. When set to zero, initialize the filter.
vpMatrix R
Measurement noise covariance matrix .
unsigned int nsignal
Number of signal to filter.
vpMatrix Q
Process noise covariance matrix .
void filtering(const vpColVector &z)
vpColVector Xest
unsigned int size_measure
Size of the measure vector .
vpMatrix H
Matrix that describes the evolution of the measurements.
void init(unsigned int size_state, unsigned int size_measure, unsigned int n_signal)
void initStateConstVel_MeasurePos(unsigned int nsignal, vpColVector &sigma_state, vpColVector &sigma_measure, double dt)
void initFilter(unsigned int nsignal, vpColVector &sigma_state, vpColVector &sigma_measure, double rho, double dt)
void initStateConstAccWithColoredNoise_MeasureVel(unsigned int nsignal, vpColVector &sigma_state, vpColVector &sigma_measure, double rho, double dt)
void initStateConstVelWithColoredNoise_MeasureVel(unsigned int nsignal, vpColVector &sigma_state, vpColVector &sigma_measure, double rho)
#define vpERROR_TRACE
Definition: vpDebug.h:382