Efficient ground object segmentation in 3D LIDAR based on cascaded mode seeking
Lidar segmentation is a common preprocessing step for performing ground object detection in autonomous vehicle applications. However, many common approaches are affected by at least one of the following three issues: they are prone to under-segmentation/over-segmentation; they are not able to effectively handle missing points caused by black objects or bright sunlight; or they are too complex for real time use on commodity hardware. In this paper we propose a modification to distance based segmentation algorithms, which is able to improve the performance without incurring significant computational cost. Numerical evaluations on the KITTI dataset confirm the methods applicability.