This work presents methods for tracking objects from noisy and unreliable data taken by a team of robots. We develop a multi-object tracking algorithm based on Kalman filtering and a single-object tracking method involving a combination of Kalman filtering and Markov localization for outlier detection. We apply these methods in the context of robot soccer for robots participating in the RoboCup middle-size league and compare them to a simple averaging method. Results including situations from real competition games are presented.
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