Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the navigation system is able to recognize the predictive uncertainty of the perception model and respond safely and effectively in the face of uncertainty. In an effort to enable safe navigation under perception uncertainty, we develop a probabilistic and reconstruction-based competency estimation (PaRCE) method to estimate the model's level of familiarity with an input image as a whole and with specific regions in the image. We find that the overall competency score can correctly predict correctly classified, misclassified, and out-of-distribution (OOD) samples. We also confirm that the regional competency maps can accurately distinguish between familiar and unfamiliar regions across images. We then use this competency information to develop a planning and control scheme that enables effective navigation while maintaining a low probability of error. We find that the competency-aware scheme greatly reduces the number of collisions with unfamiliar obstacles, compared to a baseline controller with no competency awareness. Furthermore, the regional competency information is very valuable in enabling efficient navigation.

翻译：基于感知的导航系统在复杂地形下的无人地面车辆导航中具有重要价值，传统基于深度的导航方案在此类场景中往往不足。然而，这些数据驱动方法高度依赖训练数据，可能在无预警情况下以意外且严重的方式失效。为确保车辆及周边环境的安全，导航系统必须能够识别感知模型的预测不确定性，并在面对不确定性时做出安全有效的响应。为实现感知不确定性下的安全导航，本文提出一种基于概率与重建的能力评估方法，用于评估模型对输入图像整体及图像特定区域的熟悉程度。研究发现，整体能力评分能有效区分正确分类、错误分类及分布外样本。同时验证了区域能力图能够准确识别图像中熟悉与不熟悉的区域。基于此能力信息，我们进一步设计了一种规划与控制策略，在保持低误差概率的同时实现高效导航。实验表明，相较于无能力感知的基线控制器，具备能力感知的导航方案显著降低了与陌生障碍物的碰撞次数。此外，区域能力信息对实现高效导航具有重要价值。