RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence

Road surface geometry provides information about navigable space in autonomous driving. Ground plane estimation is done on "road" points after semantic segmentation of three-dimensional (3D) automotive LiDAR point clouds as a precursor to this geometry extraction. However, the actual geometry extraction is less explored, as it is expensive to use all "road" points for mesh generation. Thus, we propose a coarser surface approximation using road edge points. The geometry extraction for the entire sequence of a trajectory provides the complete road geometry, from the point of view of the ego-vehicle. Thus, we propose an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence). Our novel approach involves ground point detection and road geometry classification, i.e. frame classification, for determining the road edge points. We use appropriate supervised and pre-trained transfer learning models, along with computational geometry algorithms to implement the workflow. Our results on SemanticKITTI show that our extracted road surface for the sequence is qualitatively and quantitatively close to the reference trajectory.