In map merging in multi-robot cooperative SLAM (simultaneous localization and mapping), global map construction maybe fails due to information deficiency caused by limited communication range or communication topology changes of the multi-robot network. To solve the problem, a new dynamic map merging algorithm is proposed based on the consensus of information gain. The proposed algorithm is fully distributed and independent of any specific communication topology. The information gain between the new observed data and the history data of the local map estimated by each robot is calculated and utilized to enable each robot to achieve consentaneous global map simultaneously. The proposed algorithm can asymptotically converge to the true global map under limited communication conditions. Furthermore, the estimated global map of each robot is unbiased in each iteration step. RGB-D (RGB-depth) data collected from real world are used to confirm the efficiency of the proposed algorithm.
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