Global localization of multirobot formations using ceiling vision SLAM strategy

Abstract

In this paper we successfully extend the single robot ceiling vision SLAM to multirobot formations for addressing global localization problem. A public global map shared by every robot is built for positioning update. Two global localization strategies are proposed. The first strategy is to globally localize one robot only and then localize the others based on the relative poses amongst the robots. The second strategy is to globally localize all the robots simultaneously. The former requires less computational resource, and the later exhibits better localization performance. Simulation experiments are finally performed to demonstrate the effectiveness of the proposed approach.