Research/ PLANETARY ROBOTICS
Deep Monocular Hazard Detection for Safe Small Body Landing
Travis Driver, Kento Tomita, Koki Ho, Panagiotis Tsiotras
Perception & State EstimationarXiv: cs.CV · Learning-based MethodsOpenAlex topic: Advanced Neural Network Applications
OSIRIS-RExnamed in abstract, keywords
arXivcs.CV · cs.RO · eess.IVOpenAlexAdvanced Neural Network Applications · Robotics and Sensor-Based Localization · Planetary Science and Exploration
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.
Comments: Presented at the AAS/AIAA Space Flight Mechanics Meeting, January 14-19, 2023, Austin, TX, USA