lidar_pattern.cpp

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/*
  velo2cam_calibration - Automatic calibration algorithm for extrinsic
  parameters of a stereo camera and a velodyne Copyright (C) 2017-2021 Jorge
  Beltran, Carlos Guindel
 
  This file is part of velo2cam_calibration.
 
  velo2cam_calibration is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 2 of the License, or
  (at your option) any later version.
 
  velo2cam_calibration is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.
 
  You should have received a copy of the GNU General Public License
  along with velo2cam_calibration.  If not, see <http://www.gnu.org/licenses/>.
*/
 
/*
  lidar_pattern: Find the circle centers in the lidar cloud
*/
 
#define PCL_NO_PRECOMPILE
 
#include <dynamic_reconfigure/server.h>
#include <pcl/common/eigen.h>
#include <pcl/common/transforms.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl_conversions/pcl_conversions.h>
#include <ros/ros.h>
#include <sensor_msgs/PointCloud2.h>
#include <std_msgs/Empty.h>
#include <velo2cam_calibration/ClusterCentroids.h>
#include <velo2cam_calibration/LidarConfig.h>
#include <velo2cam_utils.h>
 
using namespace std;
using namespace sensor_msgs;
 
ros::Publisher cumulative_pub, centers_pub, pattern_pub, range_pub, coeff_pub,
    rotated_pattern_pub;
pcl::PointCloud<pcl::PointXYZ>::Ptr cumulative_cloud;
 
// Dynamic parameters
double threshold_;
double passthrough_radius_min_, passthrough_radius_max_, circle_radius_,
    centroid_distance_min_, centroid_distance_max_;
double delta_width_circles_, delta_height_circles_;
int rings_count_;
Eigen::Vector3f axis_;
double angle_threshold_;
double cluster_tolerance_;
int clouds_proc_ = 0, clouds_used_ = 0;
int min_centers_found_;
double plane_threshold_;
double gradient_threshold_;
double plane_distance_inliers_;
double circle_threshold_;
double target_radius_tolerance_;
double min_cluster_factor_;
bool skip_warmup_;
bool save_to_file_;
std::ofstream savefile;
 
bool WARMUP_DONE = false;
 
void callback(const PointCloud2::ConstPtr &laser_cloud) {
  if (DEBUG) ROS_INFO("[LiDAR] Processing cloud...");
 
  pcl::PointCloud<Velodyne::Point>::Ptr velocloud(
      new pcl::PointCloud<Velodyne::Point>),
      velo_filtered(new pcl::PointCloud<Velodyne::Point>),
      pattern_cloud(new pcl::PointCloud<Velodyne::Point>),
      edges_cloud(new pcl::PointCloud<Velodyne::Point>);
 
  clouds_proc_++;
 
  // This cloud is already xyz-filtered
  fromROSMsg(*laser_cloud, *velocloud);
 
  Velodyne::addRange(*velocloud);
 
  // Range passthrough filter
  pcl::PassThrough<Velodyne::Point> pass2;
  pass2.setInputCloud(velocloud);
  pass2.setFilterFieldName("range");
  pass2.setFilterLimits(passthrough_radius_min_, passthrough_radius_max_);
  pass2.filter(*velo_filtered);
 
  // Publishing "range_filtered_velo" cloud (segmented plane)
  sensor_msgs::PointCloud2 range_ros;
  pcl::toROSMsg(*velo_filtered, range_ros);
  range_ros.header = laser_cloud->header;
  range_pub.publish(range_ros);
 
  // Plane segmentation
  pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
 
  pcl::SACSegmentation<Velodyne::Point> plane_segmentation;
  plane_segmentation.setModelType(pcl::SACMODEL_PARALLEL_PLANE);
  plane_segmentation.setDistanceThreshold(plane_threshold_);
  plane_segmentation.setMethodType(pcl::SAC_RANSAC);
  plane_segmentation.setAxis(Eigen::Vector3f(axis_[0], axis_[1], axis_[2]));
  plane_segmentation.setEpsAngle(angle_threshold_);
  plane_segmentation.setOptimizeCoefficients(true);
  plane_segmentation.setMaxIterations(1000);
  plane_segmentation.setInputCloud(velo_filtered);
  plane_segmentation.segment(*inliers, *coefficients);
 
  if (inliers->indices.size() == 0) {
    // Exit 1: plane not found
    ROS_WARN(
        "[LiDAR] Could not estimate a planar model for the given dataset.");
    return;
  }
 
  // Copy coefficients to proper object for further filtering
  Eigen::VectorXf coefficients_v(4);
  coefficients_v(0) = coefficients->values[0];
  coefficients_v(1) = coefficients->values[1];
  coefficients_v(2) = coefficients->values[2];
  coefficients_v(3) = coefficients->values[3];
 
  // Get edges points by range
  vector<vector<Velodyne::Point *>> rings =
      Velodyne::getRings(*velocloud, rings_count_);
  for (vector<vector<Velodyne::Point *>>::iterator ring = rings.begin();
       ring < rings.end(); ++ring) {
    Velodyne::Point *prev, *succ;
    if (ring->empty()) continue;
 
    (*ring->begin())->intensity = 0;
    (*(ring->end() - 1))->intensity = 0;
    for (vector<Velodyne::Point *>::iterator pt = ring->begin() + 1;
         pt < ring->end() - 1; pt++) {
      Velodyne::Point *prev = *(pt - 1);
      Velodyne::Point *succ = *(pt + 1);
      (*pt)->intensity =
          max(max(prev->range - (*pt)->range, succ->range - (*pt)->range), 0.f);
    }
  }
 
  float THRESHOLD =
      gradient_threshold_;  // 10 cm between the pattern and the background
  for (pcl::PointCloud<Velodyne::Point>::iterator pt =
           velocloud->points.begin();
       pt < velocloud->points.end(); ++pt) {
    if (pt->intensity > THRESHOLD) {
      edges_cloud->push_back(*pt);
    }
  }
 
  if (edges_cloud->points.size() == 0) {
    // Exit 2: pattern edges not found
    ROS_WARN("[LiDAR] Could not detect pattern edges.");
    return;
  }
 
  // Get points belonging to plane in pattern pointcloud
  pcl::SampleConsensusModelPlane<Velodyne::Point>::Ptr dit(
      new pcl::SampleConsensusModelPlane<Velodyne::Point>(edges_cloud));
  std::vector<int> inliers2;
  dit->selectWithinDistance(coefficients_v, plane_distance_inliers_, inliers2);
  pcl::copyPointCloud<Velodyne::Point>(*edges_cloud, inliers2, *pattern_cloud);
 
  // Velodyne specific info no longer needed for calibration
  // so standard PointXYZ is used from now on
  pcl::PointCloud<pcl::PointXYZ>::Ptr circles_cloud(
      new pcl::PointCloud<pcl::PointXYZ>),
      xy_cloud(new pcl::PointCloud<pcl::PointXYZ>),
      aux_cloud(new pcl::PointCloud<pcl::PointXYZ>),
      auxrotated_cloud(new pcl::PointCloud<pcl::PointXYZ>),
      cloud_f(new pcl::PointCloud<pcl::PointXYZ>),
      centroid_candidates(new pcl::PointCloud<pcl::PointXYZ>);
 
  vector<vector<Velodyne::Point *>> rings2 =
      Velodyne::getRings(*pattern_cloud, rings_count_);
 
  // Conversion from Velodyne::Point to pcl::PointXYZ
  for (vector<vector<Velodyne::Point *>>::iterator ring = rings2.begin();
       ring < rings2.end(); ++ring) {
    for (vector<Velodyne::Point *>::iterator pt = ring->begin();
         pt < ring->end(); ++pt) {
      pcl::PointXYZ point;
      point.x = (*pt)->x;
      point.y = (*pt)->y;
      point.z = (*pt)->z;
      circles_cloud->push_back(point);
    }
  }
 
  // Publishing "pattern_circles" cloud (points belonging to the detected plane)
  if (DEBUG) {
    sensor_msgs::PointCloud2 velocloud_ros2;
    pcl::toROSMsg(*circles_cloud, velocloud_ros2);
    velocloud_ros2.header = laser_cloud->header;
    pattern_pub.publish(velocloud_ros2);
  }
 
  // Rotate cloud to face pattern plane
  Eigen::Vector3f xy_plane_normal_vector, floor_plane_normal_vector;
  xy_plane_normal_vector[0] = 0.0;
  xy_plane_normal_vector[1] = 0.0;
  xy_plane_normal_vector[2] = -1.0;
 
  floor_plane_normal_vector[0] = coefficients->values[0];
  floor_plane_normal_vector[1] = coefficients->values[1];
  floor_plane_normal_vector[2] = coefficients->values[2];
 
  Eigen::Affine3f rotation =
      getRotationMatrix(floor_plane_normal_vector, xy_plane_normal_vector);
  pcl::transformPointCloud(*circles_cloud, *xy_cloud, rotation);
 
  // Publishing "rotated_pattern" cloud (plane transformed to be aligned with
  // XY)
  if (DEBUG) {
    sensor_msgs::PointCloud2 ros_rotated_pattern;
    pcl::toROSMsg(*xy_cloud, ros_rotated_pattern);
    ros_rotated_pattern.header = laser_cloud->header;
    rotated_pattern_pub.publish(ros_rotated_pattern);
  }
 
  // Force pattern points to belong to computed plane
  pcl::PointXYZ aux_point;
  aux_point.x = 0;
  aux_point.y = 0;
  aux_point.z = (-coefficients_v(3) / coefficients_v(2));
  aux_cloud->push_back(aux_point);
  pcl::transformPointCloud(*aux_cloud, *auxrotated_cloud, rotation);
  double zcoord_xyplane = auxrotated_cloud->at(0).z;
 
  for (pcl::PointCloud<pcl::PointXYZ>::iterator pt = xy_cloud->points.begin();
       pt < xy_cloud->points.end(); ++pt) {
    pt->z = zcoord_xyplane;
  }
 
  // RANSAC circle detection
  pcl::ModelCoefficients::Ptr coefficients3(new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers3(new pcl::PointIndices);
 
  pcl::SACSegmentation<pcl::PointXYZ> circle_segmentation;
  circle_segmentation.setModelType(pcl::SACMODEL_CIRCLE2D);
  circle_segmentation.setDistanceThreshold(circle_threshold_);
  circle_segmentation.setMethodType(pcl::SAC_RANSAC);
  circle_segmentation.setOptimizeCoefficients(true);
  circle_segmentation.setMaxIterations(1000);
  circle_segmentation.setRadiusLimits(
      circle_radius_ - target_radius_tolerance_,
      circle_radius_ + target_radius_tolerance_);
 
  pcl::ExtractIndices<pcl::PointXYZ> extract;
  if (DEBUG)
    ROS_INFO("[LiDAR] Searching for points in cloud of size %lu",
             xy_cloud->points.size());
  while (xy_cloud->points.size() > 3) {
    circle_segmentation.setInputCloud(xy_cloud);
    circle_segmentation.segment(*inliers3, *coefficients3);
    if (inliers3->indices.size() == 0) {
      if (DEBUG)
        ROS_INFO(
            "[LiDAR] Optimized circle segmentation failed, trying unoptimized "
            "version");
      circle_segmentation.setOptimizeCoefficients(false);
 
      circle_segmentation.setInputCloud(xy_cloud);
      circle_segmentation.segment(*inliers3, *coefficients3);
 
      // Reset for next iteration
      circle_segmentation.setOptimizeCoefficients(true);
      if (inliers3->indices.size() == 0) {
        break;
      }
    }
 
    // Add center point to cloud
    pcl::PointXYZ center;
    center.x = *coefficients3->values.begin();
    center.y = *(coefficients3->values.begin() + 1);
    center.z = zcoord_xyplane;
 
    centroid_candidates->push_back(center);
 
    // Remove inliers from pattern cloud to find next circle
    extract.setInputCloud(xy_cloud);
    extract.setIndices(inliers3);
    extract.setNegative(true);
    extract.filter(*cloud_f);
    xy_cloud.swap(cloud_f);
 
    if (DEBUG)
      ROS_INFO("[LiDAR] Remaining points in cloud %lu",
               xy_cloud->points.size());
  }
 
  if (centroid_candidates->size() < TARGET_NUM_CIRCLES) {
    // Exit 3: all centers not found
    ROS_WARN("[LiDAR] Not enough centers: %ld", centroid_candidates->size());
    return;
  }
 
  std::vector<std::vector<int>> groups;
  comb(centroid_candidates->size(), TARGET_NUM_CIRCLES, groups);
  double groups_scores[groups.size()];  // -1: invalid; 0-1 normalized score
  for (int i = 0; i < groups.size(); ++i) {
    std::vector<pcl::PointXYZ> candidates;
    // Build candidates set
    for (int j = 0; j < groups[i].size(); ++j) {
      pcl::PointXYZ center;
      center.x = centroid_candidates->at(groups[i][j]).x;
      center.y = centroid_candidates->at(groups[i][j]).y;
      center.z = centroid_candidates->at(groups[i][j]).z;
      candidates.push_back(center);
    }
 
    // Compute candidates score
    Square square_candidate(candidates, delta_width_circles_,
                            delta_height_circles_);
    groups_scores[i] = square_candidate.is_valid()
                           ? 1.0
                           : -1;  // -1 when it's not valid, 1 otherwise
  }
 
  int best_candidate_idx = -1;
  double best_candidate_score = -1;
  for (int i = 0; i < groups.size(); ++i) {
    if (best_candidate_score == 1 && groups_scores[i] == 1) {
      // Exit 4: Several candidates fit target's geometry
      ROS_ERROR(
          "[LiDAR] More than one set of candidates fit target's geometry. "
          "Please, make sure your parameters are well set. Exiting callback");
      return;
    }
    if (groups_scores[i] > best_candidate_score) {
      best_candidate_score = groups_scores[i];
      best_candidate_idx = i;
    }
  }
 
  if (best_candidate_idx == -1) {
    // Exit 5: No candidates fit target's geometry
    ROS_WARN(
        "[LiDAR] Unable to find a candidate set that matches target's "
        "geometry");
    return;
  }
 
  // Build selected centers set
  pcl::PointCloud<pcl::PointXYZ>::Ptr rotated_back_cloud(
      new pcl::PointCloud<pcl::PointXYZ>());
  for (int j = 0; j < groups[best_candidate_idx].size(); ++j) {
    pcl::PointXYZ center_rotated_back = pcl::transformPoint(
        centroid_candidates->at(groups[best_candidate_idx][j]),
        rotation.inverse());
    center_rotated_back.x = (-coefficients->values[1] * center_rotated_back.y -
                             coefficients->values[2] * center_rotated_back.z -
                             coefficients->values[3]) /
                            coefficients->values[0];
 
    rotated_back_cloud->push_back(center_rotated_back);
    cumulative_cloud->push_back(center_rotated_back);
  }
 
  if (save_to_file_) {
    std::vector<pcl::PointXYZ> sorted_centers;
    sortPatternCenters(rotated_back_cloud, sorted_centers);
    for (std::vector<pcl::PointXYZ>::iterator it = sorted_centers.begin();
         it < sorted_centers.end(); ++it) {
      savefile << it->x << ", " << it->y << ", " << it->z << ", ";
    }
  }
 
  // Publishing "cumulative_cloud" cloud (centers found from the beginning)
  if (DEBUG) {
    sensor_msgs::PointCloud2 ros_pointcloud;
    pcl::toROSMsg(*cumulative_cloud, ros_pointcloud);
    ros_pointcloud.header = laser_cloud->header;
    cumulative_pub.publish(ros_pointcloud);
  }
 
  xy_cloud.reset();  // Free memory
  cloud_f.reset();   // Free memory
 
  ++clouds_used_;
 
  // Publishing "plane_model"
  pcl_msgs::ModelCoefficients m_coeff;
  pcl_conversions::moveFromPCL(*coefficients, m_coeff);
  m_coeff.header = laser_cloud->header;
  coeff_pub.publish(m_coeff);
 
  if (DEBUG)
    ROS_INFO("[LiDAR] %d/%d frames: %ld pts in cloud", clouds_used_,
             clouds_proc_, cumulative_cloud->points.size());
 
  // Create cloud for publishing centers
  pcl::PointCloud<pcl::PointXYZ>::Ptr centers_cloud(
      new pcl::PointCloud<pcl::PointXYZ>);
 
  // Compute circles centers
  if (!WARMUP_DONE) {  // Compute clusters from detections in the latest frame
    getCenterClusters(cumulative_cloud, centers_cloud, cluster_tolerance_, 1,
                      1);
  } else {  // Use cumulative information from previous frames
    getCenterClusters(cumulative_cloud, centers_cloud, cluster_tolerance_,
                      min_cluster_factor_ * clouds_used_, clouds_used_);
    if (centers_cloud->points.size() > TARGET_NUM_CIRCLES) {
      getCenterClusters(cumulative_cloud, centers_cloud, cluster_tolerance_,
                        3.0 * clouds_used_ / 4.0, clouds_used_);
    }
  }
 
  // Exit 6: clustering failed
  if (centers_cloud->points.size() == TARGET_NUM_CIRCLES) {
    sensor_msgs::PointCloud2 ros2_pointcloud;
    pcl::toROSMsg(*centers_cloud, ros2_pointcloud);
    ros2_pointcloud.header = laser_cloud->header;
 
    velo2cam_calibration::ClusterCentroids to_send;
    to_send.header = laser_cloud->header;
    to_send.cluster_iterations = clouds_used_;
    to_send.total_iterations = clouds_proc_;
    to_send.cloud = ros2_pointcloud;
 
    centers_pub.publish(to_send);
    if (DEBUG) ROS_INFO("[LiDAR] Pattern centers published");
 
    if (save_to_file_) {
      std::vector<pcl::PointXYZ> sorted_centers;
      sortPatternCenters(centers_cloud, sorted_centers);
      for (std::vector<pcl::PointXYZ>::iterator it = sorted_centers.begin();
           it < sorted_centers.end(); ++it) {
        savefile << it->x << ", " << it->y << ", " << it->z << ", ";
      }
      savefile << cumulative_cloud->width;
    }
  }
 
  if (save_to_file_) {
    savefile << endl;
  }
 
  // Clear cumulative cloud during warm-up phase
  if (!WARMUP_DONE) {
    cumulative_cloud->clear();
    clouds_proc_ = 0;
    clouds_used_ = 0;
  }
}
 
void param_callback(velo2cam_calibration::LidarConfig &config, uint32_t level) {
  passthrough_radius_min_ = config.passthrough_radius_min;
  ROS_INFO("[LiDAR] New passthrough_radius_min_ threshold: %f",
           passthrough_radius_min_);
  passthrough_radius_max_ = config.passthrough_radius_max;
  ROS_INFO("[LiDAR] New passthrough_radius_max_ threshold: %f",
           passthrough_radius_max_);
  circle_radius_ = config.circle_radius;
  ROS_INFO("[LiDAR] New pattern circle radius: %f", circle_radius_);
  axis_[0] = config.x;
  axis_[1] = config.y;
  axis_[2] = config.z;
  ROS_INFO("[LiDAR] New normal axis for plane segmentation: %f, %f, %f",
           axis_[0], axis_[1], axis_[2]);
  angle_threshold_ = config.angle_threshold;
  ROS_INFO("[LiDAR] New angle threshold: %f", angle_threshold_);
  centroid_distance_min_ = config.centroid_distance_min;
  ROS_INFO("[LiDAR] New minimum distance between centroids: %f",
           centroid_distance_min_);
  centroid_distance_max_ = config.centroid_distance_max;
  ROS_INFO("[LiDAR] New maximum distance between centroids: %f",
           centroid_distance_max_);
}
 
void warmup_callback(const std_msgs::Empty::ConstPtr &msg) {
  WARMUP_DONE = !WARMUP_DONE;
  if (WARMUP_DONE) {
    ROS_INFO("[LiDAR] Warm up done, pattern detection started");
  } else {
    ROS_INFO("[LiDAR] Detection stopped. Warm up mode activated");
  }
}
 
int main(int argc, char **argv) {
  ros::init(argc, argv, "lidar_pattern");
  ros::NodeHandle nh;        // GLOBAL
  ros::NodeHandle nh_("~");  // LOCAL
  ros::Subscriber sub = nh_.subscribe("cloud1", 1, callback);
  pcl::console::setVerbosityLevel(pcl::console::L_ALWAYS);
 
  range_pub = nh_.advertise<PointCloud2>("range_filtered_cloud", 1);
  if (DEBUG) {
    pattern_pub = nh_.advertise<PointCloud2>("pattern_circles", 1);
    rotated_pattern_pub = nh_.advertise<PointCloud2>("rotated_pattern", 1);
    cumulative_pub = nh_.advertise<PointCloud2>("cumulative_cloud", 1);
  }
  centers_pub =
      nh_.advertise<velo2cam_calibration::ClusterCentroids>("centers_cloud", 1);
  coeff_pub = nh_.advertise<pcl_msgs::ModelCoefficients>("plane_model", 1);
 
  string csv_name;
 
  nh.param("delta_width_circles", delta_width_circles_, 0.5);
  nh.param("delta_height_circles", delta_height_circles_, 0.4);
  nh_.param("plane_threshold", plane_threshold_, 0.1);
  nh_.param("gradient_threshold", gradient_threshold_, 0.1);
  nh_.param("plane_distance_inliers", plane_distance_inliers_, 0.1);
  nh_.param("circle_threshold", circle_threshold_, 0.05);
  nh_.param("target_radius_tolerance", target_radius_tolerance_, 0.01);
  nh_.param("cluster_tolerance", cluster_tolerance_, 0.05);
  nh_.param("min_centers_found", min_centers_found_, TARGET_NUM_CIRCLES);
  nh_.param("min_cluster_factor", min_cluster_factor_, 0.5);
  nh_.param("rings_count", rings_count_, 64);
  nh_.param("skip_warmup", skip_warmup_, false);
  nh_.param("save_to_file", save_to_file_, false);
  nh_.param("csv_name", csv_name,
            "lidar_pattern_" + currentDateTime() + ".csv");
 
  cumulative_cloud =
      pcl::PointCloud<pcl::PointXYZ>::Ptr(new pcl::PointCloud<pcl::PointXYZ>);
 
  dynamic_reconfigure::Server<velo2cam_calibration::LidarConfig> server;
  dynamic_reconfigure::Server<velo2cam_calibration::LidarConfig>::CallbackType
      f;
  f = boost::bind(param_callback, _1, _2);
  server.setCallback(f);
 
  ros::Subscriber warmup_sub =
      nh.subscribe("warmup_switch", 1, warmup_callback);
 
  if (skip_warmup_) {
    ROS_WARN("Skipping warmup");
    WARMUP_DONE = true;
  }
 
  // Just for statistics
  if (save_to_file_) {
    ostringstream os;
    os << getenv("HOME") << "/v2c_experiments/" << csv_name;
    if (save_to_file_) {
      if (DEBUG) ROS_INFO("Opening %s", os.str().c_str());
      savefile.open(os.str().c_str());
      savefile << "det1_x, det1_y, det1_z, det2_x, det2_y, det2_z, det3_x, "
                  "det3_y, det3_z, det4_x, det4_y, det4_z, cent1_x, cent1_y, "
                  "cent1_z, cent2_x, cent2_y, cent2_z, cent3_x, cent3_y, "
                  "cent3_z, cent4_x, cent4_y, cent4_z, it"
               << endl;
    }
  }
 
  ros::spin();
  return 0;
}