刘俊承, 原魁, 邹伟, 朱海兵. 基于特征粒子的Monte Carlo自定位方法[J]. 机器人, 2006, 28(1): 30-35..
LIU Jun-cheng, YUAN Kui, ZOU Wei, ZHU Hai-bing. Monte Carlo Self-localization Based on Characteristic Particles. ROBOT, 2006, 28(1): 30-35..
摘要提出了一种基于Monte Carlo方法的多机器人自定位方法．该方法在机器人进行自定位时，对用来估计机器人位置的MCL（Monte Carlo Localization）粒子空间进行栅格划分，然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集．因为特征粒子的数量较粒子总数大大减少，该方法能避免直接将Monte Carlo方法应用于多机器人定位中产生的维数灾的问题，可以在保证精度的情况下降低运算复杂度．仿真结果表明，该方法能较好地满足多机器人自定位的要求．
Abstract：An approach for multi-robot self-localization based on Monte Carlo method is described.In the approach,grid cells are used to describe the whole particles space that is used in MCL(Monte Carlo Localization) method to estimate the pose of robot,then variable grid cells are used to extract the characteristic particles that can represent the whole property of the particles on estimating the robot pose.Because the number of characteristic particles is greatly less than the number of whole particles,the problem of overabundant dimensionalities caused by directly using MCL in collaborative localization of multi-robot can be solved.This approach can reduce the complexity of computation while keeping the precision of estimation.The simulation results show that it can get good collaborative localization performance in multi-robot system.
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