Abstract: Map construction of large scene can be completed efficiently through multi-robot cooperation. However, the quality of map construction deeply depends on loop closure detection results among multiple robots. Compared to single robot case, the multi-robot loop closure detection problem is more complex and difficult. The proposed method decomposes the multi-robot loop closure detection problem into two independent problems, the problem of loop closure existence and the problem of multi-robot data matching under the condition of the loop closure existence. For the problem of loop closure existence, an enforced loop closure strategy is proposed, which is setting an enforced loop closure area in the scene and enforcing multiple robots to access through. On this basis, the data matching problem across multiple robots is formulated into the hidden Markov model (HMM) based sequence matching problem. By modelling the initial matching results, state transition probability, and emission probability in the HMM model, accurate matching of data sequences collected by different robots can be accomplished. Finally, a multi-robot collaborative mapping method based on large-scale pose graph optimization (PGO) model is proposed, which uses sequence matching results to complete constraint construction and realize multi-robot collaborative mapping. The standard test database and the actual field data collected by multiple robots were used to validate the proposed algorithm. Experimental results show that the proposed multi-robot collaborative mapping method not only effectively improves the mapping efficiency and automation, but also ensures high mapping quality. The results also demonstrate the proposed algorithm outperformances traditional mapping methods.

Key words: large scale scene mapping, multi-robot, hidden Markov model, pose graph optimization