int find_planes(const OpenROVmessages::LaserMsg msg){
ROS_INFO("Entered find_planes");
// Fill cloud with data
fill_cloud(msg);
// Find planes in the cloud
while(temp_cloud->points.size() > 0.1 * nr_points){
seg.setModelType (pcl::SACMODEL_PERPENDICULAR_PLANE);
seg.setOptimizeCoefficients (true);
// Set method and threshold
seg.setMethodType (pcl::SAC_RANSAC);
seg.setDistanceThreshold (msg.ransac_threshold);
// Set the axis for which to search for perpendicular planes
Eigen::Vector3f axis = Eigen::Vector3f(1.0,0.0,0.0);
// Set a allowed deviation angle from vector axis
seg.setEpsAngle((60*CV_PI)/180);
seg.setAxis(axis);
// Segment the planes and insert the coefficients and inliers in vectors
seg.setInputCloud (temp_cloud);
seg.segment (*inliers, *coefficients);
// Check that we found a plane from the cloud
if (inliers->indices.size () == 0)
{
std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
break;
}
// push coeffients onto stack
coef_vect.push_back(*coefficients);
// Extract the inliers
extract.setInputCloud (temp_cloud);
extract.setIndices (inliers);
// Extract found inliers from cloud
extract.setNegative(true);
extract.filter(*temp_cloud);
}
return 0;
}
bool compute_object(const int i, std::vector<pcl::PointIndices > &segments) {
// Call get_plane, get_cylinder, get_sphere
// float plane_score = compute_plane(); // plane_inliers_ has the pc.
// float cylinder_score = compute_cylinder(); // cylinder_inliers_ has the pc
// Select the lowest score and store it in object_inliers_.
// inliers_object_ = (plane_score > cylinder_score ? inliers_plane_ : inliers_cylinder_);
inliers_object_ = inliers_plane_;
// inliers_object_ = inliers_cylinder_;
// Test if the point cloud is too small
if (inliers_object_->indices.size() < CUT_THRESHOLD) {
std::cerr << "object inliers has " << inliers_object_->indices.size()
<< " < " << CUT_THRESHOLD << " points. Aborting..." << std::endl;
return false;
}
/*
// Debugging info
pcl::PointCloud<Point>::Ptr best_object_(new pcl::PointCloud<Point>);
pcl::copyPointCloud(*cloud_filtered, *inliers_object_, *best_object_);
std::stringstream debug_s0;
debug_s0 << "no_cluster_object_" << i << ".pcd";
pcl::io::savePCDFile(debug_s0.str(), *best_object_);
*/
// Euclidean Clustering
std::vector<pcl::PointIndices> clusters;
cluster_.setInputCloud(cloud_filtered);
cluster_.setIndices(inliers_object_);
cluster_.extract(clusters);
std::cout << "Number of Euclidian clusters found: " << clusters.size() << std::endl;
// Check if there is no clusters
if (clusters.size() == 0) {
std::cerr << "No clusters found. Aborting..." << std::endl;
return false;
}
main_cluster_.reset(new pcl::PointIndices(clusters.at(0)));
// Extract the inliers from the input cloud
extract.setInputCloud(cloud_filtered);
extract.setIndices(main_cluster_);
extract.setNegative(false);
pcl::PointCloud<Point>::Ptr cloud_plane(new pcl::PointCloud<Point > ());
extract.filter(*cloud_plane);
// Test if cluster[0] is too small
if (main_cluster_->indices.size() < CUT_THRESHOLD) {
std::cerr << "object cluster[0] has " << main_cluster_->indices.size()
<< " < " << CUT_THRESHOLD << " points. Not writing to disk..." << std::endl;
} else {
// Write the inliers to disk
std::stringstream debug_s;
if (inliers_object_ == inliers_cylinder_) {
debug_s << "cylinder_object_" << i << ".pcd";
} else if (inliers_object_ == inliers_plane_) {
debug_s << "plane_object_" << i << ".pcd";
} else {
debug_s << "unknown_object_" << i << ".pcd";
}
pcl::io::savePCDFile(debug_s.str(), *cloud_plane);
std::cout << "PointCloud representing the extracted component: " <<
cloud_plane->points.size() << " data points." << std::endl;
// Insert the points to segments for cfg3D
pcl::PointIndices::Ptr originalIndices(new pcl::PointIndices());
originalIndices->indices.resize(cloud_plane->size());
for (unsigned int j = 0; j < originalIndices->indices.size(); j++) {
originalIndices->indices.at(j) = cloud_plane->points.at(j).rgba;
}
segments.push_back(*originalIndices);
// Debugging for cfg3D
pcl::PointCloud<Point>::Ptr cfg_segment_cloud(new pcl::PointCloud<Point > ());
pcl::ExtractIndices<Point> cfg_extract;
cfg_extract.setNegative(false);
cfg_extract.setInputCloud(cloud);
cfg_extract.setIndices(originalIndices);
cfg_extract.filter(*cfg_segment_cloud);
/*
cfg_segment_cloud->points.resize(originalIndices->indices.size());
for (unsigned int j = 0; j < originalIndices->indices.size(); j++) {
unsigned int index = originalIndices->indices.at(j);
cfg_segment_cloud->points.at(j).x = cloud->points.at(index).x;
cfg_segment_cloud->points.at(j).y = cloud->points.at(index).y;
cfg_segment_cloud->points.at(j).z = cloud->points.at(index).z;
}
*/
std::stringstream cfg_debug_s;
cfg_debug_s << "cfg_debug_segment_" << i << ".pcd";
pcl::io::savePCDFile(cfg_debug_s.str(), *cfg_segment_cloud);
}
// Remove cluster[0], update cloud_filtered and cloud_normals
extract.setNegative(true);
extract.filter(*cloud_filtered);
extract_normals.setNegative(true);
extract_normals.setInputCloud(cloud_normals);
extract_normals.setIndices(main_cluster_);
extract_normals.filter(*cloud_normals);
return true;
}
bool compute_object(const int i) {
// Call get_plane, get_cylinder, get_sphere
float plane_score = compute_plane(); // plane_inliers_ has the pc.
float cylinder_score = compute_cylinder(); // cylinder_inliers_ has the pc
// Select the lowest score and store it in object_inliers_.
inliers_object_ = (plane_score > cylinder_score ? inliers_plane_ : inliers_cylinder_);
// inliers_object_ = inliers_plane_;
// inliers_object_ = inliers_cylinder_;
// Test if the point cloud is too small
if (inliers_object_->indices.size() < CUT_THRESHOLD) {
std::cerr << "object inliers has "<< inliers_object_->indices.size()
<< " < " << CUT_THRESHOLD <<" points. Aborting..." << std::endl;
return false;
}
/*
// Debugging info
pcl::PointCloud<Point>::Ptr best_object_(new pcl::PointCloud<Point>);
pcl::copyPointCloud(*cloud_filtered, *inliers_object_, *best_object_);
std::stringstream debug_s0;
debug_s0 << "no_cluster_object_" << i << ".pcd";
pcl::io::savePCDFile(debug_s0.str(), *best_object_);
*/
// Euclidean Clustering
std::vector<pcl::PointIndices> clusters;
cluster_.setInputCloud(cloud_filtered);
cluster_.setIndices(inliers_object_);
cluster_.extract(clusters);
std::cout << "Number of Euclidian clusters found: " << clusters.size() << std::endl;
// Check if there is no clusters
if (clusters.size() == 0) {
std::cerr << "No clusters found. Aborting..." << std::endl;
return false;
}
main_cluster_.reset(new pcl::PointIndices(clusters.at(0)));
// Extract the inliers from the input cloud
extract.setInputCloud(cloud_filtered);
extract.setIndices(main_cluster_);
extract.setNegative(false);
pcl::PointCloud<Point>::Ptr cloud_plane(new pcl::PointCloud<Point > ());
extract.filter(*cloud_plane);
// Test if cluster[0] is too small
if (main_cluster_->indices.size() < CUT_THRESHOLD) {
std::cerr << "object cluster[0] has "<< main_cluster_->indices.size()
<< " < " << CUT_THRESHOLD <<" points. Not writing to disk..." << std::endl;
} else {
// Write the inliers to disk
std::stringstream debug_s;
if (inliers_object_ == inliers_cylinder_) {
debug_s << "cylinder_object_" << i << ".pcd";
} else if (inliers_object_ == inliers_plane_) {
debug_s << "plane_object_" << i << ".pcd";
} else {
debug_s << "unknown_object_" << i << ".pcd";
}
pcl::io::savePCDFile(debug_s.str(), *cloud_plane);
std::cout << "PointCloud representing the extracted component: " <<
cloud_plane->points.size() << " data points." << std::endl;
}
// Remove cluster[0], update cloud_filtered and cloud_normals
extract.setNegative(true);
extract.filter(*cloud_filtered);
extract_normals.setNegative(true);
extract_normals.setInputCloud(cloud_normals);
extract_normals.setIndices(main_cluster_);
extract_normals.filter(*cloud_normals);
return true;
}