Change-Robust Online Spatial-Semantic Topological Mapping

• URL: http://arxiv.org/abs/2605.02227v1
• Date: Mon, 04 May 2026 05:00:12 GMT
• Title: Change-Robust Online Spatial-Semantic Topological Mapping
• Abstract summary: Autonomous robots require change-robust-semantic reasoning to decide where to go, how to get there, and where the robot is despite environmental change.<n>Existing approaches typically attach semantics to SLAM-built metric maps, but these pipelines are brittle under appearance shifts and scene dynamics.<n>We propose a Change-Robust Online Spatial-Semantic representation that replaces a globally consistent metric substrate with an online, pose-aware topological graph of RGB-D rearrangements.
• Score: 10.541004576272163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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