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.

翻译：危险检测与规避是未来机器人小天体采样返回及着陆器任务的关键技术。现有先进实践方法依赖于高保真度先验地形图，这需要大量人工参与验证及昂贵的勘测活动来解决制图不确定性。我们提出了一种基于深度语义分割技术的新型安全映射范式，通过单目图像直接预测着陆安全性，从而减少对高保真先验数据产品的依赖。我们利用OSIRIS-REx任务中潜在采样点的实地影像，展示了精确且准确的安全映射性能。