Multi-robot SLAM using ceiling vision

In this paper we present a new vision-based SLAM approach for multi-robot formulation. For a cooperative map reconstruction, the robots have to know each other's relative poses, but estimating these at the start of operation puts a limit on real applications. In our study, the robots start the single SLAM with their own global coordinate, and merge their maps during the operation by detecting the overlapped region of their maps. The robots automatically recognize the occurrence of map overlapping by matching their current frame with the maps built by other robots. With the robust data association technique from the ceiling-vision based SLAM, the proposed algorithm robustly detects the overlapping regions and estimates the accurate transformations for map alignment. In our experiment, we have verified that our algorithm successfully enables the multi-robot SLAM without any initial correspondence or encounter of robots.