lidar_measurement_model_likelihood.cpp

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/*
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#include <algorithm>
#include <cmath>
#include <string>
#include <vector>

#include <ros/ros.h>

#include <pcl/point_types.h>
#include <pcl_ros/point_cloud.h>

#include <mcl_3dl/pf.h>
#include <mcl_3dl/point_cloud_random_sampler.h>
#include <mcl_3dl/point_types.h>
#include <mcl_3dl/vec3.h>

#include <mcl_3dl/lidar_measurement_models/lidar_measurement_model_likelihood.h>

namespace mcl_3dl
{
void LidarMeasurementModelLikelihood::loadConfig(
    const ros::NodeHandle& nh,
    const std::string& name)
{
  ros::NodeHandle pnh(nh, name);

  int num_points, num_points_global;
  pnh.param("num_points", num_points, 96);
  pnh.param("num_points_global", num_points_global, 8);
  num_points_default_ = num_points_ = num_points;
  num_points_global_ = num_points_global;

  double clip_near, clip_far;
  pnh.param("clip_near", clip_near, 0.5);
  pnh.param("clip_far", clip_far, 10.0);
  clip_near_sq_ = clip_near * clip_near;
  clip_far_sq_ = clip_far * clip_far;

  double clip_z_min, clip_z_max;
  pnh.param("clip_z_min", clip_z_min, -2.0);
  pnh.param("clip_z_max", clip_z_max, 2.0);
  clip_z_min_ = clip_z_min;
  clip_z_max_ = clip_z_max;

  double match_weight;
  pnh.param("match_weight", match_weight, 5.0);
  match_weight_ = match_weight;

  double match_dist_min, match_dist_flat;
  pnh.param("match_dist_min", match_dist_min, 0.2);
  pnh.param("match_dist_flat", match_dist_flat, 0.05);
  match_dist_min_ = match_dist_min;
  match_dist_flat_ = match_dist_flat;
}
void LidarMeasurementModelLikelihood::setGlobalLocalizationStatus(
    const size_t num_particles,
    const size_t current_num_particles)
{
  if (current_num_particles <= num_particles)
  {
    num_points_ = num_points_default_;
    return;
  }
  size_t num = num_points_default_ * num_particles / current_num_particles;
  if (num < num_points_global_)
    num = num_points_global_;

  num_points_ = num;
}

typename pcl::PointCloud<LidarMeasurementModelBase::PointType>::Ptr
LidarMeasurementModelLikelihood::filter(
    const typename pcl::PointCloud<LidarMeasurementModelBase::PointType>::ConstPtr& pc) const
{
  const auto local_points_filter = [this](const LidarMeasurementModelBase::PointType& p)
  {
    if (p.x * p.x + p.y * p.y > clip_far_sq_)
      return true;
    if (p.x * p.x + p.y * p.y < clip_near_sq_)
      return true;
    if (p.z < clip_z_min_ || clip_z_max_ < p.z)
      return true;
    return false;
  };
  pcl::PointCloud<LidarMeasurementModelBase::PointType>::Ptr pc_filtered(
      new pcl::PointCloud<LidarMeasurementModelBase::PointType>);
  *pc_filtered = *pc;
  pc_filtered->erase(
      std::remove_if(pc_filtered->begin(), pc_filtered->end(), local_points_filter), pc_filtered->end());
  pc_filtered->width = 1;
  pc_filtered->height = pc_filtered->points.size();

  return sampler_->sample(pc_filtered, num_points_);
}

LidarMeasurementResult LidarMeasurementModelLikelihood::measure(
    typename ChunkedKdtree<LidarMeasurementModelBase::PointType>::Ptr& kdtree,
    const typename pcl::PointCloud<LidarMeasurementModelBase::PointType>::ConstPtr& pc,
    const std::vector<Vec3>& origins,
    const State6DOF& s) const
{
  if (!pc)
    return LidarMeasurementResult(1, 0);
  if (pc->size() == 0)
    return LidarMeasurementResult(1, 0);
  pcl::PointCloud<LidarMeasurementModelBase::PointType>::Ptr pc_particle(
      new pcl::PointCloud<LidarMeasurementModelBase::PointType>);
  std::vector<int> id(1);
  std::vector<float> sqdist(1);

  float score_like = 0;
  *pc_particle = *pc;
  s.transform(*pc_particle);
  size_t num = 0;
  for (auto& p : pc_particle->points)
  {
    if (kdtree->radiusSearch(p, match_dist_min_, id, sqdist, 1))
    {
      const float dist = match_dist_min_ - std::max(std::sqrt(sqdist[0]), match_dist_flat_);
      if (dist < 0.0)
        continue;

      score_like += dist * match_weight_;
      num++;
    }
  }
  const float match_ratio = static_cast<float>(num) / pc_particle->points.size();

  return LidarMeasurementResult(score_like, match_ratio);
}
}  // namespace mcl_3dl