S.E.C. Goga, S. Nedevschi
Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 309-315.
This paper proposes a novel approach for segmenting and space partitioning data ofsparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable of dealing with both protruding and hanging objects found in urban scenes like bridges, hanging road barrier, traffic tunnels, tree branches over road surface, and so on. For this we use a MultiVolume grid representation of the environment. We apply a fast primary classifier in order to label the surface volumes as being part of the ground segment or of an object segment. Segmentation is performed on the object labeled data which is previously connected in a spatial graph structure using a height overlapping criterion. A comparison between the proposed method and the popular connected-components based segmentation method applied on an Elevation Map is performed in the end.