This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover's Distance. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.

翻译：本文提出了一种基于点云场景变化检测的算法，旨在支持未来太空舱中自主机器人维护任务。自主机器人系统将有助于维护未来深空栖息地（如Gateway空间站），这些设施在长期无人值守期间需要持续维护。当前国际空间站（ISS）使用的场景分析软件依赖人工标注图像进行变化检测，而本文提出的算法直接以原始未标注点云作为输入。该算法首先将改进的期望最大化高斯混合模型（GMM）聚类应用于两组输入点云，随后通过地球移动距离比较GMM实现变化检测。基于Astrobee机器人在NASA艾姆斯花岗岩实验室采集的测试数据集进行定量与定性验证：数据包含Astrobee直接拍摄的单帧深度图像，以及结合RGB-D与Astrobee位姿数据重建的全场景地图。本文还深度分析了算法的运行时间，并公开发布源代码以促进后续研究。