Ground Segmentation Based on Loopy Belief Propagation for Sparse 3D Point Clouds

Ground segmentation is an important pre-processing task for local environment perception using 3D LIDAR, and it is particularly challenging in unstructured environments with rough or sloped terrain. To solve the ground segmentation problem we propose a novel cost-based ground measurement model that is incorporated into a Markov Random Field and solved using loopy belief propagation. Our cost-based measurements operate on columns of a cylindrically-binned map of the LIDAR points and provide robust, non-parametric estimates for ground height. These estimates can model ambiguous situations as well as occlusions from nearer objects. A multi-label Markov Random Field in polar coordinates incorporates local smoothness and slope assumptions to filter out obstacles, while at the same time allowing sharp discontinuities in ground height when waranted by the measurements. An efficient loopy belief propagation method is used to solve for the maximum belief ground height at each cell. Experimental results show good performance in rough terrain, particularly in comparison to other local ground segmentation methods.