北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (1): 218-226.doi: 10.13700/j.bh.1001-5965.2018.0272

• 论文 • 上一篇    

基于全局稀疏地图的AGV视觉定位技术

张浩悦, 程晓琦, 刘畅, 孙军华   

  1. 北京航空航天大学 仪器科学与光电工程学院, 北京 100083
  • 收稿日期:2018-05-15 修回日期:2018-08-10 出版日期:2019-01-20 发布日期:2019-01-28
  • 通讯作者: 孙军华 E-mail:sjh@buaa.edu.cn
  • 作者简介:张浩悦,男,硕士研究生。主要研究方向:机器视觉、三维重建;孙军华,男,博士,教授,博士生导师。主要研究方向:机器视觉、精密视觉测量。

Visual localization technology of AGV based on global sparse map

ZHANG Haoyue, CHENG Xiaoqi, LIU Chang, SUN Junhua   

  1. School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
  • Received:2018-05-15 Revised:2018-08-10 Online:2019-01-20 Published:2019-01-28

摘要: 为了实现自动导引车(AGV)在复杂工业环境下的高精度定位,克服环境变化给定位带来的影响,提出了基于全局稀疏地图的视觉定位方法。首先,设计了大容量二维编码点,作为人工路标铺设在工业环境的地面;然后,基于一种四边形识别算法,在复杂工业环境中准确分割和识别二维编码点;最后,利用二维编码点提供的编码信息,鲁棒匹配图像中的特征点,并以此为基础,使用一种分参数块优化的三维重建策略,实现了工业环境的大规模地图构建,为AGV视觉定位提供了一种稀疏电子地图。AGV视觉的定位通过匹配车载视觉传感器图像中的特征点和稀疏电子地图实现。停车重复定位精度小于0.5 mm,角度偏差小于0.5°,轨迹平均位移误差小于0.1%。实际应用结果表明,该方法能在复杂工业环境中实现AGV视觉的定位,定位的速度和精度方面都满足工业应用的要求,为AGV的视觉定位提供了新的思路。

关键词: 自动导引车(AGV), 视觉定位, 三维重建, 稀疏地图, 编码点

Abstract: In order to realize the high-precision localization of automated guided vehicle (AGV) in complex industrial environment and overcome the influence of environment change, a vision localization method based on a global sparse map was proposed. First, a large-capacity two-dimensional coded point was designed, which was set on the ground as an artificial landmark. Based on a quad recognition algorithm, the coded points were accurately segmented and identified in complex industrial environment. The feature points from different images were properly matched by using the coded information provided by coded points. Then, a block-optimization three-dimensional reconstruction algorithm was designed to build a map for a large-scale industrial environment, which provided a sparse electronic map for AGV visual localization. The visual localization of AGV was realized by matching the feature points from the visual sensor and sparse electronic maps. The repeated precision of AGV is less than 0.5 mm, the angle deviation is less than 0.5°, and the average displacement error of trajectory is less than 0.1%. The practical application shows that the method can realize the visual localization of AGV in complex industrial environment. The speed and precision of localization both meet the requirements of industrial application, which provides a new way for vision-based localization of AGV.

Key words: automated guided vehicle (AGV), visual localization, three-dimensional reconstruction, sparse map, coded point

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发