A-SPAM: A Novel Asynchronous Semantic Padding and Matching Integrated Framework for Dynamic Loop Closure Detection

Abstract

Loop closure detection in dynamic SLAM faces critical challenges when dynamic objects dominate camera views, degrading frame-to-frame methods reliant on static landmarks. We propose A-SPAM, an asynchronous framework that constructs spatiotemporal semantic graphs via semantic padding (entity tracking + rigid structure analysis) and validates loops via semantic matching (topology-feature hybrid correlation). Evaluated on TUM and BONN datasets, A-SPAM achieves at least 76.8% recall rate at 100% precision in dynamic environments, while maintaining a mean translational error of less than 0.07m across dynamic sequences under degraded odometry conditions. The proposed framework corrects erroneous trajectories and enhances robustness against odometry failures in dynamic environments.