Abstract
Multi-robot SLAM (simultaneous localization and mapping) is crucial for the implementation of robots in practical scenarios. Bandwidth constraints significantly influence multi-robot SLAM systems, prompting a reliance on lightweight feature descriptors for robot cooperation in positioning tasks. Real-time map sharing among robots is also frequently ignored in such systems. Consequently, such algorithms are not feasible for autonomous multi-robot navigation tasks in the real world. Furthermore, the computation cost of the global optimization of multi-robot SLAM increases significantly in large-scale scenes. In this study, we introduce a novel distributed multi-robot SLAM framework incorporating sliding window-based optimization to mitigate computation loads and manage inter-robot loop closure constraints. In particular, we transmit a 2.5D grid map of the keyframe-based submap between robots to promote map consistency among robots and maintain bandwidth efficiency in data exchange. The proposed algorithm was evaluated in extensive experimental environments, and the results validate its effectiveness and superiority over other mainstream methods.
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