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Extended Kalman filter based localization for a mobile robot team

This work studies localization problem for a multi-robot team with one leader and some followers. A two stage localization algorithm is developed for the team's localization. In the first stage, two followers estimate their relative positions and headings regarding to a landmark based on the measurements from local sensors on these robots, and than the team leader estimates its position and heading based on measurements from local sensors and follower's state estimation. Here, extended Kalman filter algorithm is used to deal with the nonlinearities in the localization problems. The simulation results show that the localization problem can be solved by the proposed method efficiently.