As autonomous systems increasingly rely on Deep Neural Networks (DNN) to implement the navigation pipeline functions, uncertainty estimation methods have become paramount for estimating confidence in DNN predictions. Bayesian Deep Learning (BDL) offers a principled approach to model uncertainties in DNNs. However, DNN components from autonomous systems partially capture uncertainty, or more importantly, the uncertainty effect in downstream tasks is ignored. This paper provides a method to capture the overall system uncertainty in a UAV navigation task. In particular, we study the effect of the uncertainty from perception representations in downstream control predictions. Moreover, we leverage the uncertainty in the system's output to improve control decisions that positively impact the UAV's performance on its task.
翻译:由于自主系统日益依赖深神经网络来履行导航管道功能,不确定性估计方法已成为估计对DNN预测的信心的首要因素。Bayesian Deep Learning(BDL)为DNN的不确定性模型提供了一个原则性方法。然而,自发系统的DNN组件部分地捕捉了不确定性,或更重要的是,对下游任务的不确定性效应置之不理。本文件提供了一种方法,用以捕捉UAV导航任务中整个系统的不确定性。我们特别研究了下游控制预测中感知代表的不确定性的影响。此外,我们利用系统产出的不确定性来改进控制决定,从而积极影响UAV的工作。