基于快速仿射模板匹配的AMCL算法  

Adaptive Monte Carlo localization algorithm based on fast affine template matching

作　　者：张淑芳 李亚阳 张涛 ZHANG Shufang;LI Yayang;ZHANG Tao(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电气自动化与信息工程学院,天津300072

出　　处：《北京航空航天大学学报》2023年第11期2898-2905,共8页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：自主定位是移动机器人的重要任务,机器人绑架问题是定位技术中的难点。基于粒子滤波的自适应蒙特卡罗定位(AMCL)算法能够解决机器人绑架问题,但其在定位恢复过程中需要在全局地图中放入新粒子,恢复效率低。通过对自适应蒙特卡罗定位算法的研究,结合图像学中模板匹配思想,提出了一种基于快速仿射模板匹配的自适应蒙特卡罗定位(AMCL-FM)算法。该算法利用全局代价地图与局部代价地图估计出机器人的真实位置,并在估计出的位置放置新的粒子,提高定位恢复能力。与传统的自适应蒙特卡罗定位算法相比,所提算法定位精度提升了61.13%,定位恢复效率提升了69.23%。Autonomous positioning is an important task of mobile robots,and the problem of robot kidnapping is a difficult point in positioning technology.The adaptive Monte Carlo localization(AMCL)algorithm based on particle filtering can solve the problem of robot kidnapping,but it needed to put new particles on the global map during the positioning recovery process,resulting in low recovery efficiency.An adaptive Monte Carlo localization technique based on fast affine template matching(AMCL-FM)is proposed through research on the adaptive Monte Carlo localization algorithm and the idea of template matching in image science.The algorithm uses the global cost map and the local cost map to estimate the true position of the robot and then places new particles at the estimated position,which improves the effectiveness of the new particles.This algorithm’s positioning accuracy and positioning recovery effectiveness are both up 61.13%and 69.23%from adaptive Monte Carlo localization algorithm,respectively.

关 键 词：移动机器人 自主定位 自适应蒙特卡罗定位 机器人操作系统 模板匹配 

分 类 号：V221.3[航空宇航科学与技术—飞行器设计] TB553[理学—物理]