中文摘要：

针对RoboCup四腿组比赛场地结构对称和特征不唯一的特点，在场地模型中对带数据校验的扩展卡尔曼滤波（EKF-V）、多假设定位（MHL）、蒙特卡洛定位（MCL）和自适应蒙特卡洛定位（A-MCL）四种算法的全局定位精度和对噪声的鲁棒性进行了仿真实验比较．实验结果表明，四种算法在噪声可估计的条件下都能达到较高的全局定位精度，而MCL和A—MCL对噪声有较高的鲁棒性，更适合应用于RoboCup四腿组比赛．

英文摘要：

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 f,eld 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 fourlegged league due to their robustness against noises.