  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.
