LiDAR Based Traversable Regions Identification Method for Off-Road UGV Driving

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LiDAR Based Traversable Regions Identification Method for Off-Road UGV Driving

Shan, Yunxiao / Fu, Yao / Chen, Xiangchun / Lin, Hongquan / Zhang, Ziquan / Lin, Jun / Huang, Kai

Traversable regions identification technology plays a crucial role in ensuring safe driving for unmanned ground vehicles in off-road environments. However, the unstructured terrain makes it challenging to identify traversable regions. To enhance the safety of off-road driving, a LiDAR-based traversable regions identification method is proposed in this paper. Firstly, a deep learning-based neural network is used to segment the traversable regions, obstacles, and vegetation. Next, an improved Gaussian Process(GP)-based modeling method is designed to model the traversable regions with a leading speed, and the obstacle point clouds are refined with a composite filter. Finally, field experiments have demonstrated that our proposed scheme outperforms existing state-of-the-art (SOTA) traditional and deep-learning-based methods in accurately identifying both road regions and obstacles, with precision improvements of up to 14% and recall improvements of up to 9%.

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Document information

Title :

LiDAR Based Traversable Regions Identification Method for Off-Road UGV Driving

Contributors:

Shan, Yunxiao (author) / Fu, Yao (author) / Chen, Xiangchun (author) / Lin, Hongquan (author) / Zhang, Ziquan (author) / Lin, Jun (author) / Huang, Kai (author)

Published in:

IEEE Transactions on Intelligent Vehicles ; 9 , 2 ; 3544-3557

Publication date :

2024-02-01

Size :

7434698 byte

ISSN :

2379-8904 , 2379-8858

DOI :

https://doi.org/10.1109/TIV.2023.3342801

Type of media :

Article (Journal)