Adaptive Monte Carlo localization algorithm based on fast affine template matching

被引：0
|
作者：
Zhang S. [1 ]
Li Y. [1 ]
Zhang T. [1 ]
机构：
[1] School of Electrical and Information Engineering, Tianjin University, Tianjin
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 11期
关键词：
adaptive Monte Carlo localization; autonomous localization; mobile robot; robot operating system; template matching;
D O I：
10.13700/j.bh.1001-5965.2022.0001
中图分类号：
学科分类号：
摘要：
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. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：2898 / 2905
页数：7
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