It is well-known in the literature that uncertainty estimation methods are required in robotic autonomous systems that include deep learning (DL) components to assess the confidence in the outputs. However, to successfully deploy DL components in autonomous systems, they should also handle uncertainty at the input rather than only at the output. In this paper, we present a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our experiments show that the proposed method improves the robustness of the navigation policy in out-of-distribution (OoD) scenarios.