void init(const OpenROVmessages::LaserMsg msg){
/********************* INITIALIZE CLOUDS *********************/
// Mandatory: Init he sizes of the point clouds
temp_cloud->width = msg.n_rois;
temp_cloud->height = 2;
temp_cloud->resize(temp_cloud->width * temp_cloud->height);
nr_points = temp_cloud->points.size();
/**************** INITIALIZE SEGMENTER ****************/
// Mandatory: use perpendicular plane to use setAxis() function
seg.setModelType (pcl::SACMODEL_PERPENDICULAR_PLANE);
// seg.setOptimizeCoefficients (true);
// Set method and threshold
seg.setMethodType (pcl::SAC_RANSAC);
// seg.setDistanceThreshold (msg.ransac_threshold);
seg.setDistanceThreshold (5);
// Set the axis for which to search for perpendicular planes
Eigen::Vector3f axis = Eigen::Vector3f(1.0,1.0,0.0);
// Set a allowed deviation angle from vector axis
seg.setEpsAngle((60*CV_PI)/180);
seg.setAxis(axis);
}
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;
}
void depthPointsCallback(const sensor_msgs::PointCloud2ConstPtr& cloud_msg) {
// Instantiate various clouds
pcl::PCLPointCloud2* cloud_intermediate = new pcl::PCLPointCloud2;
pcl::PCLPointCloud2ConstPtr cloudPtr(cloud_intermediate);
pcl::PointCloud<pcl::PointXYZ> cloud;
// Convert to PCL data type
pcl_conversions::toPCL(*cloud_msg, *cloud_intermediate);
// Apply Voxel Filter on PCLPointCloud2
vox.setInputCloud (cloudPtr);
vox.setInputCloud (cloudPtr);
vox.filter (*cloud_intermediate);
// Convert PCL::PointCloud2 to PCL::PointCloud<PointXYZ>
pcl::fromPCLPointCloud2(*cloud_intermediate, cloud);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_p = cloud.makeShared();
// Apply Passthrough Filter
pass.setFilterFieldName ("z");
pass.setFilterLimits (0.3, 1);
pass.setInputCloud (cloud_p);
//pass.setFilterLimitsNegative (true);
pass.filter (*cloud_p);
// Apply Passthrough Filter
pass.setFilterFieldName ("x");
pass.setFilterLimits (-0.2, 0.2);
pass.setInputCloud (cloud_p);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0.0, 3.0);
//pass.setFilterLimitsNegative (true);
pass.filter (*cloud_p);
// Apply Statistical Noise Filter
sor.setInputCloud (cloud_p);
sor.filter (*cloud_p);
// Planar segmentation: Remove large planes? Or extract floor?
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
int nr_points = (int) cloud_p->points.size ();
Eigen::Vector3f lol (0, 1, 0);
seg.setEpsAngle( 30.0f * (3.14f/180.0f) );
seg.setAxis(lol);
//while(cloud_p->points.size () > 0.2 * nr_points) {
sor.setInputCloud (cloud_p);
sor.filter (*cloud_p);
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZ> seg;
// Optional
seg.setOptimizeCoefficients (true);
// Mandatory
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setDistanceThreshold (0.01);
seg.setInputCloud (cloud_p);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
//break;
}
else {
/*std::cout << "Model coefficients: " << coefficients->values[0] << " "
<< coefficients->values[1] << " "
<< coefficients->values[2] << " "
<< coefficients->values[3] << "\n";*/
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud(cloud_p);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*cloud_p);
}
//}
Eigen::Vector3f lol_p (0.5f, 0, 0.5f);
seg.setAxis(lol_p);
while(cloud_p->points.size () > 0.1 * nr_points) {
seg.setInputCloud (cloud_p);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
break;
}
else {
/*std::cout << "Model coefficients: " << coefficients->values[0] << " "
<< coefficients->values[1] << " "
<< coefficients->values[2] << " "
<< coefficients->values[3] << "\n";*/
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud(cloud_p);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*cloud_p);
}
}
// Apply Statistical Noise Filter
sor.setInputCloud (cloud_p);
sor.filter (*cloud_p);
if(cloud_p->points.size() > 0) {
std::vector<pcl::PointIndices> cluster_indices;
tree->setInputCloud (cloud_p);
ec.setInputCloud (cloud_p);
ec.extract (cluster_indices);
std::cout << "Clusters detected: " << cluster_indices.size() << "\n";
// Convert to ROS data type
}
// Convert to ROS data type
sensor_msgs::PointCloud2 output;
pcl::toROSMsg(*cloud_p, output);
// Publish the data
downsample_pub.publish(output);
}