Normal Estimation for Lidar
Hello,
I am trying to estimate normals for a Lidar scan consisting of ~2000 points structured in 8 rings. My input is a sensor_msgs::PointCloud2 which I convert to a pcl::PointCloud<pcl::PointNormal>. The estimation is performed using PCLs NormalEstimation:
pcl::NormalEstimation<pcl::PointNormal, pcl::Normal> ne;
tree = boost::make_shared<pcl::search::KdTree<pcl::PointNormal>>();
ne.setSearchMethod( tree)
ne.setRadiusSearch( 0.3 );
ne.setInputCloud(cloud)
ne.compute(*normals)
This leads to very poor results with the normals pointing in wrong directions, e.g. they should be orthogonal to the walls and not parallel (see picture).
I have played around with ne.setRadiusSearch(); and ne.setKSearch() without getting satisfying results.
Does anyone have a suggestion why this fails? It works quite well for other point clouds, e.g. obtained from depth cameras.
Two more ideas:
pcl::IntegralImageNormalEstimationseems to be a good alternative, but it needs an organized point cloud as input. My point cloud is unorganized.I can also get the scans as a
sensor_msgs/LaserScanmessage which contains the distance and angle for each point. Maybe I can use this information for the normal estimation? I could not find any solutions that work with this message...
Thanks for your ideas!
EDIT:
Using setKSearch(10) improves the result a lot:
Still the closer points that lie on the walls still have wrong orientations (marked red in the picture). In this area the distance between the scan rings is smaller, but I don't see how this can affect the results - K nearest neighbor search should actually not depend on the distance to the neighbors?
