Bandwidth-Efficient Collaborative SLAM: Mutual Information for Data Selection and Finite-State Entropy for Lossless Compression

Collaborative SLAM systems allow resource-constrained robots to offload intensive computation to a central server, making real-time mapping and localization feasible. However, this approach introduces significant communication overhead, which can hinder practical deployment due to the high bandwidth required to transmit sensor data. To address this communication bottleneck in large-scale SLAM systems, we propose a novel bandwidth-optimization strategy that enables efficient collaboration between resource-constrained robots and a powerful central server. This strategy leverages mutual-information theory to select and transmit only the most informative keyframes, significantly reducing redundancy. Additionally, we apply finite-state entropy coding to achieve lossless compression of the selected data. Experiments on the public dataset and real-world scene demonstrate that our approach achieves a substantial 78% reduction in bandwidth usage compared to state-of-the-art centralized multi-robot SLAM methods, while maintaining high-precision localization and mapping performance without introducing significant computational overhead.