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 difficulty of the task. Moreover, 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 paper proposes a novel framework for cost map generation, which uses LiDAR-inertial odometry (LIO) and historical observed frames to generate local cost maps with no need for any prior information. To describe 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 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 cost map with detailed traversability descriptions even with violent UGV jolts.