We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.
@article{arxiv.2209.10995,
title = {Challenges in Visual Anomaly Detection for Mobile Robots},
author = {Dario Mantegazza and Alessandro Giusti and Luca M. Gambardella and Andrea Rizzoli and Jérôme Guzzi},
journal= {arXiv preprint arXiv:2209.10995},
year = {2022}
}
Comments
Workshop paper presented at the ICRA 2022 Workshop on Safe and Reliable Robot Autonomy under Uncertainty https://sites.google.com/umich.edu/saferobotautonomy/home