# Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

arXiv: https://arxiv.org/abs/2110.13729 · HTML: https://mlsanity.com/paper/2110.13729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty](https://mlsanity.com/paper/2409.06111/) (2024)

## Abstract (arXiv)

Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they should also handle uncertainty at the input rather than only at the output of the DL components. Considering a probability distribution in the input enables the propagation of uncertainty through different components to provide a representative measure of the overall system uncertainty. In this position paper, we propose a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our early experiments show that the proposed method improves the robustness of the navigation policy in Out-of-Distribution (OoD) scenarios.