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.

翻译：辅助性自由飞行机器人——如国际空间站（ISS）上的NASA Astrobee机器人——在自主维护未来载人前哨站时，必须具备检测内部日常变化的能力，以追踪物资、诊断故障并监测前哨站状态。本研究提出一种多智能体协同建图与变化检测框架，用于实现太空前哨站的机器人维护。一个智能体通过图像序列及对应的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条件下采集的真实图像与位姿数据，在完成扫描后对变化检测结果进行验证。本文阐述了多智能体重建系统的目标、需求及算法模块，并提出了未来微重力前哨站中辅助性自由飞行机器人的使用建议。