Comparing Sensor Fusion Techniques for Ball Position Estimation

In robotic soccer a good ball position estimate is essential for successful play. Given the uncertainties in the perception of each individual robot, merging the local perceptions of the robots into a global ball estimate often results in a more reliable estimate and helps to increase team performance. Robots can use the global ball position even if they themselves do not see the ball or they can use it to adjust their own perception faults. In this paper we report on our results of comparing state-of-the-art sensor fusion techniques like Kalman filters or the Monte Carlo approach in RoboCup's Middle-size league. We compare our results to previously published work from other Middle-size league teams and show how the quality of perceiving the ball position is increased.