2023/01/30 by Travis Driver, Kento Tomita, Driver, Travis +5
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Advanced Neural Network Applications #Space Satellite Systems and Control
paper · pdf · doi:10.48550/arxiv.2301.13254
Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.