Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
Cooperative Visual Tracking in a Team of Autonomous Mobile Robots
RoboCup 2006: Robot Soccer World Cup X
Multirobot object localization: a fuzzy fusion approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-robot team coordination through roles, positionings and coordinated procedures
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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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.