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Traversability Estimation for Off-RoadAutonomous Driving UnderEgo-Motion Uncertainty
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DOI：10.1109/JSEN.2024.3350574.png)
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摘要
En 中文
The accurate and stable estimation of travers ability is a crucial task for unmanned ground vehicle (UGV) driving in off-road environments. However, the complexity of off-road environments increases the difficulty of the task. Moreover, the stability of travers ability estimation is adversely affected by ego-motion uncertainty caused by the violent jolts when the UGV is traveling faston uneven surfaces. The lack of prior information also poses a significant challenge to the task. To address these challenges, this article proposes a novel framework forcost map (CM) generation, which uses light detection and ranging (LiDAR)-inertial odometry (LIO) and historically observed frames to generate local CMs with no need for any prior information. To describe the travers ability of complex environments, we design a cost calculation method that includes a variety of factors affecting UGV driving. Not only terrain features such as slope and roughness, but also potential slip risks are considered in it. In consideration of ego-motion uncertainty, terrain continuity is modeled as a spatial constraint to enhance sparse laser scans, and historical observations are fused to filter out noises in the temporal dimension. Real-world experimental results demonstrate that the proposed method can generate stable CM with detailed travers ability descriptions even with violent UGV jolts
Keyword:
Cost map (CM) generation
ego-motion uncertainty
traversability estimation
unmanned ground vehicle(UGV)
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
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