"Distributed Multi-Robot Localization"
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 collects 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 information 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 decentralized form therefore allowing this single Kalman filter to be decomposed into a number of smaller communicating filters each of which processes local data (regarding the particular host robot) for most of the time. The resulting decentralized estimation schema, which we call collective localization constitutes a unique mean for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent decentralized Information filter is provided.