The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the use of visual cameras, which ensure operational ability from atmospheric entry through touchdown. Plagued by resource constraints and limited computational power, traditional techniques for visual hazardous terrain detection focus on template matching and registration to pre-built hazard maps. Although successful on previous missions, this approach is restricted to the specificity of the templates and limited by the fidelity of the underlying hazard map, which both require extensive pre-flight cost and effort to obtain and develop. Terrestrial systems that perform a similar task in applications such as autonomous driving utilize state-of-the-art deep learning techniques to successfully localize and classify navigation hazards. Advancements in spacecraft co-processors aimed at accelerating deep learning inference enable the application of these methods in space for the first time. In this work, we introduce You Only Crash Once (YOCO), a deep learning-based visual hazardous terrain detection and classification technique for autonomous spacecraft planetary landings. Through the use of unsupervised domain adaptation we tailor YOCO for training by simulation, removing the need for real-world annotated data and expensive mission surveying phases. We further improve the transfer of representative terrain knowledge between simulation and the real world through visual similarity clustering. We demonstrate the utility of YOCO through a series of terrestrial and extraterrestrial simulation-to-real experiments and show substantial improvements toward the ability to both detect and accurately classify instances of planetary terrain.

翻译：航天器行星着陆过程中危险地形的检测对保障飞行器安全及任务成功至关重要。通过使用视觉相机检测危险地形是一种经济高效的方法，可确保从大气进入至触地全阶段的操作能力。受资源约束和有限计算能力的制约，传统视觉危险地形检测技术主要依赖模板匹配及与预构建危险地图的配准。尽管在先前的任务中取得了成功，但该方法受限于模板的特异性及底层危险地图的保真度，两者均需耗费大量飞行前成本与精力进行获取与开发。在自动驾驶等应用中执行类似任务的地面系统，采用最先进的深度学习技术成功实现了导航障碍物的定位与分类。面向加速深度学习推理的航天器协处理器的最新进展，首次使得这些方法在太空中的应用成为可能。本研究提出了一种基于深度学习的视觉危险地形检测与分类技术——你只会坠毁一次（YOCO），用于自主航天器行星着陆。通过无监督域自适应技术，我们针对性调整YOCO以允许基于仿真训练，从而消除对真实世界标注数据及昂贵任务勘测阶段的需求。进一步地，我们通过视觉相似性聚类提升了仿真与真实世界间代表性地形知识的迁移能力。通过一系列地面与地外场景的仿真到真实实验，我们验证了YOCO的实用性，并在行星地形的检测与精准分类能力上展现了显著提升。