Multi-robot Localization by Observation Merging

Abstract

In robot soccer, self-localization of robots may fail because of perception failure, falling down or by being pushed by another robot. In this study, our goal is to improve self localization of robots using the teammate robots' perceptions. Robots which have perceived more landmarks and have moved less can share their localization and observations with other robots to improve localization accuracy. Currently, in the RoboCup Standard Platform League it is not feasible to identify the jersey number of robots using vision. Therefore, we merge perceptions of all robots depending on their reliability, and then identify them in a probabilistic manner to increase localization performance and provide a common world model that can be used for planning. We show that our approach has a significant advantage with respect to single robot localization on estimating the poses of the robots.