Watch Their Moves: Applying Probabilistic Multiple Object Tracking to Autonomous Robot Soccer

Thorsten Schmitt, Michael Beetz, Robert Hanek, and Sebastian Buck, Munich University of Technology

In many autonomous robot applications robots must be capable of estimating the positions and motions of moving objects in their environments. In this paper, we apply probabilistic multiple object tracking to estimating the positions of opponent players in autonomous robot soccer. We extend an existing tracking algorithm to handle multiple mobile sensors with uncertain positions, discuss the specification of probabilistic models needed by the algorithm, and describe the required vision-interpretation algorithms. The multiple object tracking has been successfully applied throughout the RoboCup 2001 world championship.

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