A Combined Monte-Carlo Localization And Tracking Algorithm For Robocup

Self-localization is a major research task in mobile robotics for several years. Efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. It enables robots to localize themselves in real-time and to recover from localization errors. However, even those versions of MCL using an adaptive number of samples need at least a minimum in the order of 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL based on images from an omnidirectional camera system. The approach uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. We show that the method enters this efficient tracking mode after a few cycles and remains there using only a single sample for more than 90% of the cycles. Nevertheless, it is still able to cope with the kidnapped robot problem.