发表机构
Indian Institute of Technology Bombay（印度孟买理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多会话映射中对象级变化检测问题，提出OASIS-Map系统，通过建立语义对应维护一致对象级地图，在三个真实场景演示，取得停车场汽车替换场景变化检测F1值0.783等成果。
AI 中文摘要
跨多次访问真实世界半静态环境保持一致的地图表示对长期机器人检查非常有用。场景可能在机器人不在时演变，现有方法存在局限性。本文提出OASIS-Map，通过在时间观测之间建立密集补丁级语义对应来维护时空一致的对象级地图，检测场景变化并跨访问关联对象。在三个真实场景中进行了演示，在停车场汽车替换场景的变化检测中F1值达0.783，在3RScan中移动物体关联的F1值为0.667。
英文摘要
Map representations which are consistent across repeated visits to a real-world semi-static environment are very useful for long-term robotic inspection. In such settings, the scene may evolve while the robot is absent, with objects appearing, disappearing, moving, or being replaced, quickly making a static map outdated. Existing change-detection methods reason through geometry, category-level semantics, or object persistence. However, achieving reliable object association across revisits remains a key challenge, especially under partial views, occlusion, and imperfect segmentation. In this work, we propose OASIS-Map, a multi-session mapping system that maintains a spatio-temporally consistent object-level map by establishing dense patch-level semantic correspondences between temporal observations. These correspondences detect where the scene has changed and incrementally associate objects across revisits as the robot re-observes the environment. We demonstrate OASIS-Map on three challenging real-world scenarios: object rearrangements in 3RScan, visually similar car replacements in a car park, and large-scale scene changes in an outdoor market. We achieve 0.783 F1 on change detection in a car replacement scenario in a car park and 0.667 F1 on moved object association in 3RScan. https://dynamic.robots.ox.ac.uk/projects/oasis-map/
Comments8 pages, 6 figures, website: https://dynamic.robots.ox.ac.uk/projects/oasis-map/
