Streamlining Data Transfer in Collaborative SLAM Through Bandwidth-Aware Map Distillation

Abstract

Edge intelligence offers a promising solution for Simultaneous Localization and Mapping (SLAM) in large-scale scenarios, where multiple robots collaboratively perceive the environment and upload their local maps to an edge server. However, maintaining mapping accuracy under constrained and dynamic communication resources remains a significant challenge for the practical deployment of robot swarms. Concurrent data uploads from multiple agents can exacerbate network congestion, leading to the loss of critical information, delayed updates, and, ultimately, the inconsistency of the generated maps. This paper presents Hermes, an edge-assisted collaborative mapping system designed for communication-constrained environments. Hermes streamlines data transfer through bandwidth-aware map distillation, ensuring only the most crucial messages are transmitted to the edge server. We quantify the importance of keyframes and landmarks based on their information entropy gain in pose estimation. By selectively sharing essential submaps, Hermes adaptively balances communication bandwidth and information richness during the mapping process. We implemented Hermes on heterogeneous platforms and conducted experiments using public datasets and self-collected campus data. Hermes exceeds SwarmMap by 50% in bandwidth utilization with similar accuracy and surpasses COVINS-G by 65% in trajectory error under highly constrained network resources.