Change-Robust Online Spatial-Semantic Topological Mapping

TL;DR

This paper introduces CROSS, a change-robust topological mapping system for robots that maintains spatial-semantic reasoning despite environmental changes, outperforming traditional SLAM methods.

Contribution

The paper presents a novel online topological graph approach with explicit ambiguity reasoning and belief maintenance, improving robustness under appearance shifts and scene dynamics.

Findings

CROSS outperforms SLAM-based and topological baselines in severe appearance change scenarios.

The system effectively handles loop closures and kidnapped-robot events.

Experiments include real-robot object-goal navigation with lighting shifts and furniture rearrangement.

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.