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：辅助型自由飞行机器人（如NASA部署于国际空间站的Astrobee机器人）需自主维护未来载人前哨站，具备检测舱内日常变化、跟踪库存、诊断故障及监测前哨状态的能力。本文提出一种面向太空前哨站机器人维护的多智能体协同建图与变化检测框架：一个智能体通过图像序列及对应深度信息重建环境三维模型，另一个智能体定期扫描环境以检测与三维模型的不一致性。基于Astrobee机器人在地面测试环境及国际空间站微重力环境采集的真实图像与位姿数据，完成勘测后的变化检测验证。本文阐述了多智能体重建系统的目标、需求及算法模块，并就该系统在未来微重力前哨站辅助型自由飞行器中的应用提出建议。*表示同等贡献