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对于复杂地形下的无人地面车辆（UGV）导航非常有用，因为传统的基于深度的导航方案在此类场景中往往不足。然而，这些数据驱动的方法高度依赖于其训练数据，可能在几乎没有预警的情况下以意外且严重的方式失效。为确保车辆及周围环境的安全，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，我们开发了一种基于概率与重建的能力估计（PaRCE）方法，用于评估模型对整个输入图像以及图像中特定区域的熟悉程度。我们发现，整体能力分数能够正确预测正确分类、错误分类以及分布外（OOD）样本。我们还证实，区域能力图能够准确区分图像中熟悉与不熟悉的区域。随后，我们利用此能力信息开发了一种规划与控制方案，该方案能够在保持低错误概率的同时实现有效导航。与不具备能力感知的基线控制器相比，我们发现能力感知方案显著减少了与不熟悉障碍物的碰撞次数。此外，区域能力信息对于实现高效导航具有重要价值。