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

作者：Yunxiao Shan, Yao Fu, Xiangchun Chen, Hongquan Lin, Ziquan Zhang, Jun Lin, Kai Huang · 发表于：IEEE Transactions on Intelligent Vehicles · 年份：2023 · DOI：10.1109/tiv.2023.3342801 · 被引用次数：12 · 研究领域：Autonomous Vehicle Technology and Safety、Robotic Path Planning Algorithms、Robotics and Sensor-Based Localization

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%.