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

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 0.07 m across dynamic sequences under degraded odometry conditions. The proposed framework corrects erroneous trajectories and enhances robustness against odometry failures in dynamic environments.