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.
@article{arxiv.2206.01953,
title = {Quantifying and Using System Uncertainty in UAV Navigation},
author = {Fabio Arnez and Ansgar Radermacher and Huascar Espinoza},
journal= {arXiv preprint arXiv:2206.01953},
year = {2022}
}
Comments
Accepted at the ICRA 2022 Workshop on Releasing Robots into the Wild: Simulations, Benchmarks, and Deployment