I'm trying to find spheres from a point cloud with pcl::sacSegmentation using RANSAC. The cloud is scanned with an accurate terrestial scanner from one station. The cloud density is about 1cm. The best results so far are shown in the image below. As you can see the cloud contains 2 spheres (r=7,25cm) and a steel beam where the balls are attached.. I am able to find three sphere candidates whose inlier points are extracted from cloud in the image (You can see two circle shapes on the beam near the spheres).
Input point cloud. Inlier points extracted
So, it seems that I am close. Still the found sphere centers are too much (~10cm) away from the truth. Any suggestion how could I improve this? I have been tweaking the model parameters quite some time. Here are the parameters for the aforementioned results:
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_SPHERE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setMaxIterations(500000);
seg.setDistanceThreshold(0.0020);
seg.setProbability(0.99900);
seg.setRadiusLimits(0.06, 0.08);
seg.setInputCloud(cloud);
I also tried to improve the results by including point normals in the model with no better results. Yet there are couple parameters more to adjust so there might be some combinations I had not tried.
I happily give you more information if needed.
Thaks naikh0u
After some investigation I have come in to conclusion that I can't find spheres with SACSegmentation from a cloud that contains lot of other points that don't belong in any sphere shape. Like in my case the beam is too much for the algorithm. Thus, I have to choose the points that show some potential being a part of a sphere shape. Also I think, I need to separate the points belonging in different spheres. I tested and saw that my code works pretty well if the input cloud has only sphere points for single sphere with some "natural" noise.
Some have solved this problem by first extracting all points belonging to planes and then searched for spheres. Others have used colors of the target (in case of camera) to extract only needed points.
Deleting plane points should work for my example cloud, but my application may have more complex shapes too so it may be too simple..
..Finally, I got satisfied results by clustering the cloud with pcl::EuclideanClusterExtraction and feeding the clusters for sphere search one by one.