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As autonomous systems increasingly rely on Deep Neural Networks (DNN) toimplement the navigation pipeline functions, uncertainty estimation methodshave become paramount for estimating confidence in DNN predictions. BayesianDeep Learning (BDL) offers a principled approach to model uncertainties inDNNs. However, DNN components from autonomous systems partially captureuncertainty, or more importantly, the uncertainty effect in downstream tasks isignored. This paper provides a method to capture the overall system uncertaintyin a UAV navigation task. In particular, we study the effect of the uncertaintyfrom perception representations in downstream control predictions. Moreover, weleverage the uncertainty in the system's output to improve control decisionsthat positively impact the UAV's performance on its task.

Quantifying and Using System Uncertainty in UAV Navigation