Traversability Estimation for Off-Road Autonomous Driving Under Ego-Motion Uncertainty

The accurate and stable estimation of traversability 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 traversability estimation is adversely affected by ego-motion uncertainty caused by the violent jolts when the UGV is traveling fast on 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 for cost 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 traversability 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 traversability descriptions even with violent UGV jolts.