TY - GEN
T1 - Efficient ground object segmentation in 3D LIDAR based on cascaded mode seeking
AU - Hoy, Michael
AU - Dauwels, Justin
AU - Yuan, Junsong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85046280550
U2 - 10.1109/ITSC.2017.8317867
DO - 10.1109/ITSC.2017.8317867
M3 - Conference contribution
AN - SCOPUS:85046280550
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -