Synergetic localization for groups of mobile robots

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 thus allowing this single Kalman filter to be decomposed into a number of smaller communicating filters, each of them processing local data for most of the time. The resulting decentralized estimation scheme 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 distributed information filter is provided.