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

Abstract

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. Note to Practitioners—In real-world multi-robot SLAM systems especially for mapping tasks, limited communication bandwidth can be a serious bottleneck for central coordination. This paper introduces a bandwidth-efficient approach that allows robots to only send the most informative visual keyframes, minimizing communication overhead. By using mutual information for keyframe selection and applying lossless finite-state entropy coding, the system reduces redundancy while maintaining high accuracy in localization and mapping. This method can be integrated into existing SLAM frameworks, boosting scalability and cutting down on wireless load without sacrificing performance. Additionally, we will open-source the full code, making it easier for practitioners to deploy and use in real-world applications.