Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

• Published in: Journal of field robotics Vol. 40; no. 7; pp. 1779 - 1803
• Main Authors: Xue, Hanzhang, Fu, Hao, Xiao, Liang, Fan, Yiming, Zhao, Dawei, Dai, Bin
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.10.2023
• Subjects: Angles (geometry); Connectivity analysis; Data integration; Lidar; Modelling; obstacle detection; rough terraing; terrain information; Terrain models; unmanned ground vehicle (UGV); unstructured environment
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22209

For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial‐temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real‐time and outperforms state‐of‐the‐art approaches.