Ground Filtering of Co-Registered Mobile and Stationary Laser Scans by Using Superpoints in Ransac Planes

Ground filtering is an important tool for many applications. The high variability of landscapes makes it necessary to perform its computation with 3D points as the only input, that is, with as few as possible algorithm parameters and without any training data. In the case of terrestrial laser scans, an additional challenge comes from a highly inhomogeneous point density. The SiRP algorithm on ground filtering, relying on intermediate validating superpixels as ground or non-ground, was previously developed for airborne point clouds. We consider a dataset acquired by a mobile mapping system mounted on a car, which was extended by additional stationary laser scans. The registration algorithm is based on hierarchical merging. Afterwards, SiRP was applied to both uni-modal and multi-modal point clouds. Impressive qualitative and quantitative classification results with around 97 % on overall accuracy were obtained and discussed.