Multi-robot Cooperative Brain-inspired SLAM

The advantages of low computing and storage requirements make RatSLAM, a brain-inspired navigation model, well-suitable for constructing large-scale environmental maps. To further improve the mapping efficiency, a multi-robot collaborative RatSLAM system is proposed. Firstly, a centralized multi-robot communication system is designed. Secondly, a method for detecting environment overlapped region is proposed to achieve data association between robots, so as to calculate the relative pose between experience nodes. Finally, the proposed improved graph relaxation algorithm is used to fuse the maps of multiple robots using the pose relationship between robots, to complete the real-time online construction of a globally unified experience cognitive map. The proposed method is verified on both public datasets and in a real-world physical environment to show its effectiveness. The experimental results show that, in comparison to single robot, the average mapping efficiency of multiple robots is increased by 45%, meanwhile maintaining higher map accuracy and requiring less storage. Furthermore, the proposed approach yields a much better mapping results compared with the existing map fusion methods, confirming its effectiveness and accuracy.