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Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving
2020
APSIPA Transactions on Signal and Information Processing
In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly
doi:10.1017/atsip.2020.21
fatcat:ihfiaqckmfaghoxreojsgvoorm