tracker_kalman.cpp

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/* Author: Wim Meeussen */
/* Modified by Alex Noyvirt for SRS */

#include "srs_people_tracking_filter/tracker_kalman.h"

using namespace MatrixWrapper;
using namespace BFL;
using namespace tf;
using namespace std;
using namespace ros;


const static double damping_velocity = 0.9;


namespace estimation
{
  // constructor
  TrackerKalman::TrackerKalman(const string& name, const StatePosVel& sysnoise):
    Tracker(name),
    filter_(NULL),
    sys_pdf_(NULL),
    sys_model_(NULL),
    meas_pdf_(NULL),
    meas_model_(NULL),
    sys_matrix_(6,6),
    tracker_initialized_(false)
  {
    // create sys model
    sys_matrix_ = 0;
    for (unsigned int i=1; i<=3; i++){
      sys_matrix_(i,i) = 1;
      sys_matrix_(i+3,i+3) = damping_velocity;
    }
    ColumnVector sys_mu(6); sys_mu = 0;
    sys_sigma_ = SymmetricMatrix(6); sys_sigma_ = 0;
    for (unsigned int i=0; i<3; i++){
      sys_sigma_(i+1, i+1) = pow(sysnoise.pos_[i],2);
      sys_sigma_(i+4, i+4) = pow(sysnoise.vel_[i],2);
    }
    Gaussian sys_noise(sys_mu, sys_sigma_);
    sys_pdf_   = new LinearAnalyticConditionalGaussian(sys_matrix_, sys_noise);
    sys_model_ = new LinearAnalyticSystemModelGaussianUncertainty(sys_pdf_);


    // create meas model
    Matrix meas_matrix(3,6); meas_matrix = 0;
    for (unsigned int i=1; i<=3; i++)
      meas_matrix(i,i) = 1;

    ColumnVector meas_mu(3); meas_mu = 0;
    SymmetricMatrix meas_sigma(3); meas_sigma = 0;
    for (unsigned int i=0; i<3; i++)
      meas_sigma(i+1, i+1) = 0;
    Gaussian meas_noise(meas_mu, meas_sigma);
    meas_pdf_   = new LinearAnalyticConditionalGaussian(meas_matrix, meas_noise);
    meas_model_ = new LinearAnalyticMeasurementModelGaussianUncertainty(meas_pdf_);
  };



  // destructor
  TrackerKalman::~TrackerKalman(){
    if (filter_)      delete filter_;
    if (sys_pdf_)     delete sys_pdf_;
    if (sys_model_)   delete sys_model_;
    if (meas_pdf_)    delete meas_pdf_;
    if (meas_model_)  delete meas_model_;
  };



  // initialize prior density of filter 
  void TrackerKalman::initialize(const StatePosVel& mu, const StatePosVel& sigma, const double time)
  {
    ColumnVector mu_vec(6);
    SymmetricMatrix sigma_vec(6); sigma_vec = 0;
    for (unsigned int i=0; i<3; i++){
      mu_vec(i+1) = mu.pos_[i];
      mu_vec(i+4) = mu.vel_[i];
      sigma_vec(i+1,i+1) = pow(sigma.pos_[i],2);
      sigma_vec(i+4,i+4) = pow(sigma.vel_[i],2);
    }
    prior_ = Gaussian(mu_vec, sigma_vec);
    filter_ = new ExtendedKalmanFilter(&prior_);

    // tracker initialized
    tracker_initialized_ = true;
    quality_ = 1;
    filter_time_ = time;
    init_time_ = time;
  }




  // update filter prediction
  bool TrackerKalman::updatePrediction(const double time)
  {
    bool res = true;
    if (time > filter_time_){
      // set dt in sys model
      for (unsigned int i=1; i<=3; i++)
        sys_matrix_(i, i+3) = time - filter_time_;
      sys_pdf_->MatrixSet(0, sys_matrix_);

      // scale system noise for dt
      sys_pdf_->AdditiveNoiseSigmaSet(sys_sigma_ * pow(time - filter_time_,2));
      filter_time_ = time;

      // update filter
      res = filter_->Update(sys_model_);
      if (!res) quality_ = 0;
      else quality_ = calculateQuality();
    }
    return res;
  };



  // update filter correction
bool TrackerKalman::updateCorrection(const tf::Vector3&  meas, const MatrixWrapper::SymmetricMatrix& cov)
  {
    assert(cov.columns() == 3);

    // copy measurement
    ColumnVector meas_vec(3);
    for (unsigned int i=0; i<3; i++)
      meas_vec(i+1) = meas[i];

    // set covariance
    ((LinearAnalyticConditionalGaussian*)(meas_model_->MeasurementPdfGet()))->AdditiveNoiseSigmaSet(cov);

    // update filter
    bool res = filter_->Update(meas_model_, meas_vec);
    if (!res) quality_ = 0;
    else quality_ = calculateQuality();

    return res;
  };


  void TrackerKalman::getEstimate(StatePosVel& est) const
  {
    ColumnVector tmp = filter_->PostGet()->ExpectedValueGet();
    for (unsigned int i=0; i<3; i++){
      est.pos_[i] = tmp(i+1);
      est.vel_[i] = tmp(i+4);
    }
  };


  void TrackerKalman::getEstimate(srs_msgs::PositionMeasurement& est) const
  {
    ColumnVector tmp = filter_->PostGet()->ExpectedValueGet();

    est.pos.x = tmp(1);
    est.pos.y = tmp(2);
    est.pos.z = tmp(3);

    est.header.stamp.fromSec( filter_time_ );
    est.object_id = getName();
  }




  double TrackerKalman::calculateQuality()
  {
    double sigma_max = 0;
    SymmetricMatrix cov = filter_->PostGet()->CovarianceGet();
    for (unsigned int i=1; i<=2; i++)
      sigma_max = max(sigma_max, sqrt(cov(i,i)));

    return 1.0 - min(1.0, sigma_max / 1.5);
  }


  double TrackerKalman::getLifetime() const
  {
    if (tracker_initialized_)
      return filter_time_ - init_time_;
    else
      return 0;
  }

  double TrackerKalman::getTime() const
  {
    if (tracker_initialized_)
      return filter_time_;
    else
      return 0;
  }

}; // namespace