Abstract: Both uncertainty about the pose and the environment maps,simultaneous localization and map building（SLAM） becomes so complex in unknown environments.So this paper introduces a local optimal parameter approach：first to build local optimal environment maps by local optimal robot pose,then to find out local optimal robot pose by local optimal environment maps.Localization and map building are performed recursively until the task is completely finished.The experiment results indicate that the SLAM finished by the modified SLAM based on Particle Filtering（PF-SLAM） is more accurate than the normal PF-SLAM.It gives a new method for robot SLAM in unknown outside environments.

摘要： 针对未知环境中移动机器人同时定位和地图创建（Simultaneous Localization and Map Building,SLAM）由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优（全局次优）参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法（Particle Filtering-SLAM,PF-SLAM）比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。