Change-Robust Online Spatial-Semantic Topological Mapping
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.
Cite
@article{arxiv.2605.02227,
title = {Change-Robust Online Spatial-Semantic Topological Mapping},
author = {Jiaming Wang and Jizhuo Chen and Diwen Liu and Atharva Ghotavadekar and Jiaxuan Da and Linh Kästner and Harold Soh},
journal= {arXiv preprint arXiv:2605.02227},
year = {2026}
}