基于机器人激光定位的一种改进AMCL算法  

An Improved AMCL Algorithm Based on Robot Laser Localization

作　　者：冯佳萌 裴东[1,2] 邹勇 张博文 丁鹏 Feng Jiameng;Pei Dong;Zou Yong;Zhang Bowen;Ding Peng(College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou Gansu,730070,China;Engineering Research Center of Gansu Province for Intelligent Information Technology and Application,Lanzhou,Gansu 730070,China)

机构地区：[1]西北师范大学物理与电子工程学院,甘肃兰州730070 [2]甘肃省智能信息技术与应用工程研究中心,甘肃兰州730070

出　　处：《激光与光电子学进展》2021年第20期471-479,共9页Laser & Optoelectronics Progress

摘　　要：高效的定位算法是实现机器人自主运动的前提,由于激光模型受复杂环境的限制,传统自适应蒙特卡罗定位(AMCL)算法提供的位姿精度有限。提出一种增加扫描匹配(SM)和离散傅里叶变换(DFT)的优化AMCL算法,将传统AMCL的加权均值输出作为SM的初始值,通过构建激光雷达观测点与先验地图的匹配函数模型,利用高斯牛顿的方法优化求解,最终通过DFT滤波滤除位置处的小抖动,提升了系统的稳定性和鲁棒性。通过运动中的绝对定位实验和重复定位,实验验证了优化算法优于传统AMCL算法,优化算法有效提高了系统定位精度,同时保证了鲁棒性。An efficient localization algorithm is the prerequisite for autonomous robot movement.The traditional adaptive Monte Carlo localization(AMCL)algorithm provides low pose accuracy owing to the complex environment limiting the laser model.Herein,an optimized AMCL algorithm of scan matching(SM)and discrete Fourier transform(DFT)is presented.A weighted average output of the traditional AMCL was used as the initial value of the SM,a matching function model of the lidar observation point and previous map was constructed,and the GaussNewton method was used to optimize the solution.Finally,the minor jitter at the localization was filtered through the DFT filter,improving the system’s stability and robustness.Through absolute localization experiments and repeated localization experiments in motion,it is verified that the optimization algorithm is superior to the traditional AMCL algorithm.The optimization algorithm effectively improves the system’s localization accuracy while maintaining its robustness.

关 键 词：遥感 机器人定位 自适应蒙特卡罗定位算法 激光雷达 扫描匹配 高斯牛顿 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]