detector_particle.cpp

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/* Author: Wim Meeussen */

#include <people_tracking_filter/detector_particle.h>
#include <people_tracking_filter/uniform_vector.h>
#include <vector>

namespace estimation
{
using MatrixWrapper::Matrix;

// constructor
DetectorParticle::DetectorParticle(unsigned int num_particles):
  prior_(num_particles),
  filter_(NULL),
  sys_model_(tf::Vector3(0.1, 0.1, 0.1)),
  meas_model_(tf::Vector3(0.1, 0.1, 0.1)),
  detector_initialized_(false),
  num_particles_(num_particles)
{}

// destructor
DetectorParticle::~DetectorParticle()
{
  if (filter_) delete filter_;
}

// initialize prior density of filter
void DetectorParticle::initialize(const tf::Vector3& mu, const tf::Vector3& size, const double time)
{
  std::cout << "Initializing detector with " << num_particles_ << " particles, with uniform size "
            << size << " around " << mu << std::endl;

  BFL::UniformVector uniform_vector(mu, size);
  std::vector<BFL::Sample<tf::Vector3> > prior_samples(num_particles_);
  uniform_vector.SampleFrom(prior_samples, num_particles_, CHOLESKY, NULL);
  prior_.ListOfSamplesSet(prior_samples);
  filter_ = new BFL::BootstrapFilter<tf::Vector3, tf::Vector3>(&prior_, &prior_, 0, num_particles_ / 4.0);

  // detector initialized
  detector_initialized_ = true;
  quality_ = 1;
  filter_time_ = time;
}

// update filter prediction
bool DetectorParticle::updatePrediction(const double dt)
{
  // set de in sys model
  sys_model_.SetDt(dt);

  // update filter
  bool res = filter_->Update(&sys_model_);
  if (!res) quality_ = 0;

  return res;
}

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

  // set filter time
  filter_time_ = time;

  // set covariance
  static_cast<BFL::MeasPdfVector*>(meas_model_.MeasurementPdfGet())->CovarianceSet(cov);

  // update filter
  bool res = filter_->Update(&meas_model_, meas);
  if (!res) quality_ = 0;

  return res;
}

// get evenly spaced particle cloud
void DetectorParticle::getParticleCloud(const tf::Vector3& step, double threshold, sensor_msgs::PointCloud& cloud) const
{
  static_cast<BFL::MCPdfVector*>(filter_->PostGet())->getParticleCloud(step, threshold, cloud);
}

// get most recent filter posterior
void DetectorParticle::getEstimate(tf::Vector3& est) const
{
  est = static_cast<BFL::MCPdfVector*>(filter_->PostGet())->ExpectedValueGet();
}

void DetectorParticle::getEstimate(people_msgs::PositionMeasurement& est) const
{
  tf::Vector3 tmp = filter_->PostGet()->ExpectedValueGet();

  est.pos.x = tmp[0];
  est.pos.y = tmp[1];
  est.pos.z = tmp[2];

  est.header.stamp.fromSec(filter_time_);
  est.header.frame_id = "base_link";
}

Matrix DetectorParticle::getHistogram(const tf::Vector3& min, const tf::Vector3& max, const tf::Vector3& step) const
{
  return static_cast<BFL::MCPdfVector*>(filter_->PostGet())->getHistogram(min, max, step);
}
}  // namespace estimation