Change-Robust Online Spatial-Semantic Topological Mapping
For autonomous robots operating in dynamic environments, this work addresses the brittleness of existing SLAM-based semantic mapping under appearance shifts and perceptual aliasing.
The paper proposes a change-robust online spatial-semantic topological mapping (CROSS) system that replaces metric SLAM with a pose-aware topological graph, achieving improved robustness under severe appearance changes and scene dynamics in real-robot object-goal navigation tasks.
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.