"Synergetic Localization for Groups of Mobile Robots "
In this paper we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing each other. Each of the robots col lects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning infor mation from all the members of the team and produces a pose estimate for each of them. The equations for this centralized estimator can be written in a decentral ized form therefore allowing this single Kalman filter to be decomposed into a number of smaller communi cating filters each of them processing local (regarding the particular host robot) data for most of the time. The resulting decentralized estimation scheme consti tutes a unique mean for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localiza tion algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent distributed in formation filter is provided.