Analyzing the quality of matched 3D point clouds of objects

3D laser scanners are frequently used sensors for mobile robots or autonomous cars and they are often used to perceive the static as well as dynamic aspects in the scene. In this context, matching 3D point clouds of objects is a crucial capability. Most matching methods such as numerous flavors of ICP provide little information about the quality of the match, i.e. how well do the matched objects correspond to each other, which goes beyond point-to-point or point-to-plane distances. In this paper, we propose a projective method that yields a probabilistic measure for the quality of matched scans. It not only considers the differences in the point locations but can also take free-space information into account. Our approach provides a probabilistic measure that is meaningful enough to evaluate scans and to cluster real-world data such as scans taken with Velodyne scanner in urban scenes in an unsupervised manner.