通过语义匹配实现多轮扫描中物体级变化检测，提升机器人长期巡检的环境感知能力。
OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching
- 基于密集语义对应关系建立跨时段观测的物体关联。
- 在停车场车辆替换场景下变化检测F1达0.783，3RScan中移动物体关联F1为0.667。
- 适用于复杂遮挡、部分视角下的长期环境监控，适合机器人巡检应用。
在重复访问真实世界半静态环境时，保持地图一致性对长期机器人巡检极为重要。环境中物体可能在机器人离开期间出现、消失、移动或被替换，导致静态地图迅速过时。现有方法依赖几何、类别语义或物体持续性进行变化检测，但跨次观测的可靠物体关联仍是关键挑战，尤其在部分视野、遮挡和分割不准确的情况下。本文提出OASIS-Map，一种多时段映射系统，通过建立时序观测间的密集补丁级语义对应，实现时空一致的物体级地图维护。该方法可检测场景变化，并在机器人重新观测时增量式关联物体。我们在三个具有挑战性的现实场景中验证：3RScan中的物体重排、停车场中视觉相似的车辆替换，以及户外市场的大型场景变化。在停车场车辆替换场景中，变化检测取得0.783 F1；在3RScan中移动物体关联达到0.667 F1。
原文摘要 · Abstract (English)
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while the robot is absent, with objects appearing, disappearing, moving, or being replaced, quickly making a static map outdated.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/
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