Abstract
Cooperation of multiple robots in simultaneous localization and mapping (SLAM) systems has more advantages compared to single robot configurations such as a faster exploration speed of the environment and the ability to perform tasks with high complexity. In this paper, we present an online multi-robot SLAM system which merges range measurements provided by UWB sensors and Lidar data provided by different mobile robots to build a globally-consistent map that contains individual point cloud maps and the trajectory estimations of all the robots. The proposed system does not require overlaps between robot trajectories. However, when the maps of different robots overlap, the system can further refine the relative robot transformations. The performance of the proposed system is evaluated through two experiments. The results of the experiments show that the proposed system can achieve accurate environment mapping and self-positioning by the cooperation of multiple robots in real-time.
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