In recent years, the success of single-robot SLAM has led to more multi-robot SLAM (MR-SLAM) research. A team of robots with MR-SLAM can explore an environment more efficiently and reliably; however, MR-SLAM also raises many challenging problems, including map fusion, unknown robot poses and scalability issues. The first two problems can be considered as an optimization problem of finding a consistent joint map based on robots' relative poses and sensory data. This optimization problem exhibits

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... a similar property of a singlerobot topological/metric mapping. To exploit this property, we propose a multi-robot SLAM (MR-SLAM) algorithm, which builds a graph-like topological map with vertices representing local metric maps and edges describing relative positions of adjacent local maps. In this MR-SLAM algorithm, the map fusion between two robots can be naturally done by adding an edge that connects two topological maps, and the estimation of relative robot pose is simply performed by optimizing this edge. For the third scalable problem, the proposed algorithm is also scalable to the number of robots and the size of an environment. Computer simulations with a public data set and experimental work on Pioneer 3-DX robots have been conducted to validate the performance of the proposed MR-SLAM algorithm.