OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching

• URL: http://arxiv.org/abs/2607.14899v1
• Date: Thu, 16 Jul 2026 12:16:52 GMT
• Title: OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching
• Abstract summary: Map consistent across repeated visits to a real-world semi-static environment is useful for long-term robotic inspection.<n>Existing change-detection methods reason through geometry, category-level semantics, or object persistence.<n>We propose a multi-session mapping system that maintains a segmentation-temporally consistent object-level representations.
• Score: 2.68828933665259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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while the robot is absent, with objects appearing, disappearing, moving, or being replaced, quickly making a static map outdated.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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