Most traversability estimation techniques divide off-road terrain into traversable (e.g., pavement, gravel, and grass) and non-traversable (e.g., boulders, vegetation, and ditches) regions and then inform subsequent planners to produce trajectories on the traversable part. However, recent research demonstrated that wheeled robots can traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves), which unfortunately would be deemed as non-traversable by existing techniques. Motivated by such limitations, this work aims at identifying the traversable from the seemingly non-traversable, vertically challenging terrain based on past kinodynamic vehicle-terrain interactions in a data-driven manner. Our new Traverse the Non-Traversable(TNT) traversability estimator can efficiently guide a down-stream sampling-based planner containing a high-precision 6-DoF kinodynamic model, which becomes deployable onboard a small-scale vehicle. Additionally, the estimated traversability can also be used as a costmap to plan global and local paths without sampling. Our experiment results show that TNT can improve planning performance, efficiency, and stability by 50%, 26.7%, and 9.2% respectively on a physical robot platform.

翻译：大多数可通行性估计技术将越野地形划分为可通行区域（如铺装路面、碎石路和草地）与不可通行区域（如巨石、植被和沟壑），进而引导后续规划器在可通行区域生成轨迹。然而，近期研究表明，轮式机器人能够穿越垂直挑战地形（如尺寸与车辆本身相当的极端崎岖巨石），而现有技术却会将其误判为不可通行区域。受此局限性的启发，本研究旨在基于历史运动力学车辆-地形交互数据，以数据驱动的方式从看似不可通行的垂直挑战地形中识别可通行区域。我们提出的新型"穿越不可穿越之地"可通行性估计器能有效引导包含高精度六自由度运动力学模型的下游采样规划器，使其可部署于小型车辆平台。此外，估计所得的可通行性信息还可作为代价地图用于无需采样的全局与局部路径规划。实验结果表明，在物理机器人平台上，该估计器可将规划性能、效率与稳定性分别提升50%、26.7%和9.2%。