论文标题
无人机导航中的量化和使用系统不确定性
Quantifying and Using System Uncertainty in UAV Navigation
论文作者
论文摘要
随着自主系统越来越多地依赖深度神经网络（DNN）来实施导航管道功能，不确定性估计方法已成为估计对DNN预测信心的至关重要的。贝叶斯深度学习（BDL）提供了一种原则性的方法来模拟DNN中的不确定性。但是，自主系统的DNN组件部分捕获不确定性，或更重要的是，下游任务中的不确定性效果被忽略。本文提供了一种捕获无人机导航任务中总体系统不确定性的方法。特别是，我们研究了下游控制预测中感知表示的不确定性的影响。此外，我们利用系统输出的不确定性来改善控制决策，从而对无人机在其任务上的绩效产生积极影响。
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.
