A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing work typically assumes no wheel slip or uses the expected traction for motion planning, where the predicted trajectories provide a poor indication of the actual performance if the terrain traction has high uncertainty. In contrast, this work proposes to analyze terrain traversability with the empirical distribution of traction parameters in unicycle dynamics, which can be learned by a neural network in a self-supervised fashion. The probabilistic traction model leads to two risk-aware cost formulations that account for the worst-case expected cost and traction. To help the learned model generalize to unseen environment, terrains with features that lead to unreliable predictions are detected via a density estimator fit to the trained network's latent space and avoided via auxiliary penalties during planning. Simulation results demonstrate that the proposed approach outperforms existing work that assumes no slip or uses the expected traction in both navigation success rate and completion time. Furthermore, avoiding terrains with low density-based confidence score achieves up to 30% improvement in success rate when the learned traction model is used in a novel environment.

翻译：越野导航中的关键挑战在于，即使视觉上相似或属于同一语义类别的地形，其牵引特性也可能存在显著差异。现有工作通常假设无车轮滑移或使用期望牵引力进行运动规划，当地形牵引力存在高度不确定性时，预测轨迹无法有效反映实际性能。相比之下，本文提出利用单轮动力学中牵引参数的经验分布来分析地形可通行性，该分布可通过神经网络以自监督方式学习。基于概率牵引模型，我们提出了两种风险感知代价函数，分别考虑最坏情况下的期望代价和牵引力。为使学习模型泛化至未知环境，通过密度估计器拟合训练网络的潜在空间来检测导致不可靠预测的地形特征，并在规划中通过辅助惩罚项予以规避。仿真结果表明，所提方法在导航成功率和完成时间上均优于假设无滑移或使用期望牵引力的现有工作。此外，当学习到的牵引模型应用于新环境时，规避基于密度置信度分数较低的地形可使成功率提升高达30%。