UGV obstacle detection of sensor fusion based on point cloud data segmentation

Xin Yu; Yimei Fan; Nana Xu; Jinqi Chen; Tianding Chen

doi:10.1117/12.2617991

15 February 2022 UGV obstacle detection of sensor fusion based on point cloud data segmentation

Xin Yu, Yimei Fan, Nana Xu, Jinqi Chen, Tianding Chen

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121667J (2022) https://doi.org/10.1117/12.2617991
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

This paper uses the point cloud data extracted by LiDAR as the basis of obstacle detection and proposes an algorithm to reduce the computational complexity of point cloud. Firstly, it removes the points too high or too low from the point cloud data, leaving only the points forming the obstacle interval. Secondly, the point cloud in the interval is projected to the X-Y plane (horizontal plane) and the resolution is reduced by lattice. Finally, the connected component marker clustering method is used to segment and classify obstacles. After identifying the obstacle, the scene information is provided to the path planning algorithm. In the path planning algorithm, Unmanned Ground Vehicle (UGV) moves along the shortest straight line connecting the target point and the starting point. When encountering obstacles, the edge tracking method is used to bypass the obstacle, and then it goes straight again. After planning by the path planning algorithm, UGV can avoid collision and advance to the destination in real time through the motion control system.

The experimental result shows that UGV designed by this research has the ability to reach the target point safely, and it can avoid obstacles and move autonomously in real-time conditions and complete outdoor unknown environmental obstacle detection.

Citation Download Citation

Xin Yu, Yimei Fan, Nana Xu, Jinqi Chen, and Tianding Chen "UGV obstacle detection of sensor fusion based on point cloud data segmentation", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121667J (15 February 2022); https://doi.org/10.1117/12.2617991

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KEYWORDS

Clouds

LIDAR

Environmental sensing

Detection and tracking algorithms

Control systems

Intelligence systems

Gyroscopes

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