Change-Robust Online Spatial-Semantic Topological Mapping

2026-05-04

AI Summary

This work addresses the limited robustness of autonomous robots in spatial-semantic reasoning and localization under drastic environmental changes—such as sudden illumination shifts or object movements—by proposing an online, pose-aware topological semantic mapping approach. Departing from conventional globally consistent metric maps, the method constructs a topological graph from RGB-D keyframes and explicitly handles perceptual ambiguity through sequential hypothesis testing in continuous SE(3) space. It maintains a bounded Gaussian mixture belief over robot pose to robustly manage loop closures and kidnapping scenarios. Experimental results demonstrate that the proposed approach significantly outperforms both SLAM and topological baselines under severe appearance variations and real-world robotic navigation tasks, while preserving reliability and safety even in perceptually aliased environments.

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.

Problem

• change-robust
• spatial-semantic reasoning
• environmental change
• perceptual ambiguity
• topological mapping

Innovation

• topological mapping
• change-robust perception
• spatial-semantic reasoning
• Gaussian-mixture belief
• online SLAM