An Experimental Comparison of Localization Methods in RoboCup Four-legged League

For RoboCup four-legged league field with symmetrical structure and non-unique features,the global localization accuracy and robustness against noises of four localization methods,including Extended Kalman Filter with data Validation(EKF-V),Multiple Hypothesis Localization(MHL),Monte Carlo Localization(MCL) and Adaptive Monte Carlo Localization(A-MCL),are compared in a simulated field model.The experimental results show that all the algorithms achieve high accuracy when the noise can be estimated.However,MCL and A-MCL are preferable when applying to RoboCup four-legged league due to their robustness against noises.