  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, in DNN-based systems, not all the components use uncertainty
estimation methods and typically ignore the uncertainty propagation between
them. This paper provides a method that considers the uncertainty and the
interaction between BDL components to capture the overall system uncertainty.
We study the effect of uncertainty propagation in a BDL-based system for
autonomous aerial navigation. Experiments show that our approach allows us to
capture useful uncertainty estimates while slightly improving the system's
performance in its final task. In addition, we discuss the benefits,
challenges, and implications of adopting BDL to build dependable autonomous
systems.
