具有能力感知的规划用于在感知不确定性下的概率安全导航
Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty
AI总结：
本文提出PaRCE方法，通过估计模型对输入图像的整体和局部熟悉度，提升在感知不确定性下的导航安全性与效率。
摘要：
基于感知的导航系统在复杂地形中对无人地面车辆（UGV）导航具有用处，其中传统基于深度的导航方案不足。然而，这些数据驱动的方法高度依赖于其训练数据，并且在缺乏预警的情况下可能会以意外和剧烈的方式失败。为了确保车辆和周围环境的安全，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安全有效地做出响应。为了实现在感知不确定性下的安全导航，我们开发了一种基于概率和重建的能力估计（PaRCE）方法，以估计模型对输入图像的整体熟悉程度以及图像中的特定区域。我们发现，整体能力分数可以正确预测正确分类、误分类和超出分布（OOD）的样本。我们还确认，区域能力图可以准确区分图像中的熟悉和不熟悉区域。然后，我们利用这种能力信息开发了一种规划和控制方案，使车辆在保持低错误概率的同时实现有效导航。我们发现，具有能力感知的方案与无能力感知的基线控制器相比，大大减少了与不熟悉障碍物的碰撞次数。此外，区域能力信息在实现高效导航方面非常有价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
