NASA's forthcoming Lunar Gateway space station, which will be uncrewed most of the time, will need to operate with an unprecedented level of autonomy. One key challenge is enabling the Canadarm3, the Gateway's external robotic system, to detect hazards in its environment using its onboard inspection cameras. This task is complicated by the extreme and variable lighting conditions in space. In this paper, we introduce the visual anomaly detection and localization task for the space domain and establish a benchmark based on a synthetic dataset called ALLO (Anomaly Localization in Lunar Orbit). We show that state-of-the-art visual anomaly detection methods often fail in the space domain, motivating the need for new approaches. To address this, we propose MRAD (Model Reference Anomaly Detection), a statistical algorithm that leverages the known pose of the Canadarm3 and a CAD model of the Gateway to generate reference images of the expected scene appearance. Anomalies are then identified as deviations from this model-generated reference. On the ALLO dataset, MRAD surpasses state-of-the-art anomaly detection algorithms, achieving an AP score of 62.9% at the pixel level and an AUROC score of 75.0% at the image level. Given the low tolerance for risk in space operations and the lack of domain-specific data, we emphasize the need for novel, robust, and accurate anomaly detection methods to handle the challenging visual conditions found in lunar orbit and beyond.

翻译：美国国家航空航天局即将建成的月球门户空间站在大部分时间内将处于无人值守状态，其运行需要前所未有的自主能力。一个关键挑战在于使门户站的外部机器人系统Canadarm3能够利用其机载检测摄像头识别环境中的危险。太空环境中极端且多变的照明条件使得该任务变得尤为复杂。本文针对航天领域提出了视觉异常检测与定位任务，并基于名为ALLO（月球轨道异常定位）的合成数据集建立了基准测试。我们展示了当前最先进的视觉异常检测方法在航天领域常常失效，这凸显了开发新方法的必要性。为此，我们提出了MRAD（模型参考异常检测），这是一种统计算法，它利用已知的Canadarm3位姿和门户站的CAD模型来生成预期场景外观的参考图像。异常则被识别为与此模型生成的参考之间的偏差。在ALLO数据集上，MRAD超越了当前最先进的异常检测算法，在像素级别实现了62.9%的平均精度得分，在图像级别实现了75.0%的受试者工作特征曲线下面积得分。鉴于太空操作对风险的极低容忍度以及领域特定数据的缺乏，我们强调需要新颖、鲁棒且准确的异常检测方法，以应对月球轨道及更远太空环境中极具挑战性的视觉条件。