Localization strategies for indoor multi-robot formations

Global localization in multi-robot formations is an important issue but has not been studied sufficiently yet. In this paper, we apply a ceiling vision SLAM algorithm to a multi-robot formation system for solving the global localization problem. A feature-based matching approach is utilized to calculate the relative positions amongst the robots. Three strategies are proposed for global localization in the formation. The first strategy is to globally localize one robot only (i.e., leader) and then localize the others based on relative positions amongst the robots. The second strategy is to globally localize all the robots by having each robot implement SLAM individually. The third strategy is to globally localize all the robots using a common SLAM based on a shared global map. Experiments are performed to demonstrate the effectiveness of the proposed approaches.