Assistive free-flyer robots autonomously caring for future crewed outposts -- such as NASA's Astrobee robots on the International Space Station (ISS) -- must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts. *Denotes Equal Contribution

翻译：辅助性自由飞行机器人自主维护未来载人前哨站——例如国际空间站（ISS）上的NASA Astrobee机器人——必须具备检测日常内部变化的能力，以追踪库存、检测与诊断故障并监测前哨站状态。本研究提出了一种多智能体协同建图与变化检测框架，以实现空间前哨站的机器人自主维护。一个智能体用于从图像序列及对应深度信息中重建环境的三维模型；另一智能体则定期扫描环境，检测其与三维模型之间的不一致性。利用Astrobee机器人在地面测试环境及国际空间站微重力环境中采集的真实图像与位姿数据，在完成勘测后对变化检测进行了验证。本研究阐述了多智能体重建系统的目标、需求与算法模块，并对其在未来微重力前哨站中辅助性自由飞行机器人的应用提出了建议。*标识同等贡献