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针对移动机器人在未知环境中导航时由于机器人本身位置不确定、所处环境不可预知等问题,提出了一种在栅格地图中基于改进粒子滤波的SLAM定位算法.首先利用贝叶斯规则更新环境信息;然后利用改进粒子滤波对机器人进行定位,地图更新和机器人定位交替进行,直到将整个环境探测完毕.仿真结果表明:该算法在SLAM中增强了实时性,比较精确地估计出机器人的位姿,同时创建的栅格地图具有较高的精度,为机器人定位与地图构建的研究提供了一种可行性方案.
Aiming at the problem that the mobile robot’s position in the unknown environment is uncertain and its environment is unpredictable, this paper proposes a SLAM localization algorithm based on improved particle filter in raster map.Firstly, the Bayesian rule is used to update the environment Then, the improved particle filter is used to locate the robot, and the map is updated and the robot’s position is alternated until the entire environment is detected. The simulation results show that the algorithm can enhance the real-time performance in SLAM and estimate the pose of the robot more accurately At the same time, the created raster map has high accuracy and provides a feasible solution for robot positioning and map construction.