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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.02227 |
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| _version_ | 1866915976842313728 |
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| author | Wang, Jiaming Chen, Jizhuo Liu, Diwen Ghotavadekar, Atharva Da, Jiaxuan Kästner, Linh Soh, Harold |
| author_facet | Wang, Jiaming Chen, Jizhuo Liu, Diwen Ghotavadekar, Atharva Da, Jiaxuan Kästner, Linh Soh, Harold |
| contents | Autonomous robots require change-robust spatial-semantic reasoning: using spatial and semantic knowledge to decide where to go, how to get there, and where the robot is despite environmental change. Existing approaches typically attach semantics to SLAM-built metric maps, but these pipelines are brittle under appearance shifts and scene dynamics, where data association and relocalization degrade. We propose a Change-Robust Online Spatial-Semantic (CROSS) representation that replaces a globally consistent metric substrate with an online, pose-aware topological graph of RGB-D keyframes. The system explicitly reasons over perceptual ambiguity using sequential hypothesis testing in continuous SE(3). Our estimator maintains a bounded Gaussian-mixture belief over poses, enabling principled handling of loop closures and kidnapped-robot events. Experiments under severe appearance change, including real-robot object-goal navigation with lighting shifts and furniture rearrangement, demonstrate improved robustness over SLAM-based and topological baselines while remaining safe under perceptual aliasing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02227 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Change-Robust Online Spatial-Semantic Topological Mapping Wang, Jiaming Chen, Jizhuo Liu, Diwen Ghotavadekar, Atharva Da, Jiaxuan Kästner, Linh Soh, Harold Robotics Autonomous robots require change-robust spatial-semantic reasoning: using spatial and semantic knowledge to decide where to go, how to get there, and where the robot is despite environmental change. Existing approaches typically attach semantics to SLAM-built metric maps, but these pipelines are brittle under appearance shifts and scene dynamics, where data association and relocalization degrade. We propose a Change-Robust Online Spatial-Semantic (CROSS) representation that replaces a globally consistent metric substrate with an online, pose-aware topological graph of RGB-D keyframes. The system explicitly reasons over perceptual ambiguity using sequential hypothesis testing in continuous SE(3). Our estimator maintains a bounded Gaussian-mixture belief over poses, enabling principled handling of loop closures and kidnapped-robot events. Experiments under severe appearance change, including real-robot object-goal navigation with lighting shifts and furniture rearrangement, demonstrate improved robustness over SLAM-based and topological baselines while remaining safe under perceptual aliasing. |
| title | Change-Robust Online Spatial-Semantic Topological Mapping |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.02227 |