Abstract
Humanoid robots sensory signals typically suffer from noise. In the typical case of indoor environement, small obstacles like carpets, books, or wires can make the odomtry error degenerate which eventually results in severe inaccuracies during the localization process. In this paper, we describe a landmark based multi-robot localization architecture which handles robustly the self-localization problem of a team of small humanoid robots. The landmark detection under partial occlusion and affine transformations takes advantage of the real-time capabilities of a random ferns based vision system. The observations of a single robot and those of cooperating parteners are merged through a particle filter-based method. In our approach, the abslute localization of every single robot is achieved in a robot-centric way. The relative localization is kept by a remote machine. Since all the team data is interfaced via the remote machine, every robot can act independently from the rest of the robot network.
