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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

张浩悦, 程晓琦, 刘畅, 等 . 基于全局稀疏地图的AGV视觉定位技术[J]. 北京航空航天大学学报, 2019, 45(1): 218-226. doi: 10.13700/j.bh.1001-5965.2018.0272
引用本文: 张浩悦, 程晓琦, 刘畅, 等 . 基于全局稀疏地图的AGV视觉定位技术[J]. 北京航空航天大学学报, 2019, 45(1): 218-226. doi: 10.13700/j.bh.1001-5965.2018.0272
ZHANG Haoyue, CHENG Xiaoqi, LIU Chang, et al. Visual localization technology of AGV based on global sparse map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 218-226. doi: 10.13700/j.bh.1001-5965.2018.0272(in Chinese)
Citation: ZHANG Haoyue, CHENG Xiaoqi, LIU Chang, et al. Visual localization technology of AGV based on global sparse map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 218-226. doi: 10.13700/j.bh.1001-5965.2018.0272(in Chinese)

基于全局稀疏地图的AGV视觉定位技术

doi: 10.13700/j.bh.1001-5965.2018.0272
详细信息
    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

    通讯作者:

    孙军华, E-mail: sjh@buaa.edu.cn

  • 中图分类号: V249.325;TP242.2

Visual localization technology of AGV based on global sparse map

More Information
  • 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。

     

  • 图 1  编码点

    Figure 1.  Coded points

    图 2  二维编码点的检测和识别

    Figure 2.  Detection and recognition of two-dimensional coded points

    图 3  全局稀疏地图重建流程图

    Figure 3.  Flowchart of global sparse map reconstruction

    图 4  坐标系转换关系

    Figure 4.  Transformation relationship of coordinate system

    图 5  用于重构的原始图像

    Figure 5.  Original image for reconstruction

    图 6  用于定位的稀疏地图

    Figure 6.  Sparse map for localization

    图 7  测量值和重建值结果的对比

    Figure 7.  Comparison of measurement and reconstruction results

    图 8  搭载视觉定位系统的AGV

    Figure 8.  AGV equipped with visual localization system

    图 9  静态定位实验

    Figure 9.  Experiment of static localization

    图 10  行驶轨迹

    Figure 10.  Travelling trajectory

    表  1  二维编码点边长重构统计结果

    Table  1.   Statistical results of two-dimensional code side length reconstitution

    mm
    参数 平均值 平均误差 标准差
    数值 27.98 0.05 0.08
    下载: 导出CSV

    表  2  动态定位结果

    Table  2.   Result of dynamic localization

    车位 测量次数 x坐标/mm y坐标/mm 角度/(°)
    B201 1 31 814.1 13 511.2 89.4
    2 31 813.9 13 512.1 89.5
    3 31 814.0 13 512.0 89.6
    4 31 813.9 13 511.8 90.3
    5 31 814.0 13 511.7 89.4
    6 31 813.8 13 511.9 89.9
    7 31 814.1 13 512.0 89.8
    8 31 813.9 13 511.9 89.8
    9 31 813.9 13 511.8 89.6
    10 31 813.7 13 511.8 89.6
    B104 1 34 600.7 1 154.9 270.5
    2 34 600.7 1 154.9 270.3
    3 34 601.6 1 155.3 270.5
    4 34 601.7 1 155.2 270.8
    5 34 601.3 1 155.4 271.1
    6 34 601.7 1 155.2 270.3
    7 34 601.5 1 154.9 270.8
    8 34 600.9 1 154.8 270.8
    9 34 600.9 1 154.7 270.7
    10 34 600.8 1 155.1 271.2
    下载: 导出CSV

    表  3  定位轨迹的误差

    Table  3.   Error of localization trajectory

    %
    比较次数 位移误差 平均位移误差
    1 0.071 0.082
    2 0.084
    3 0.082
    4 0.087
    5 0.082
    6 0.084
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-15
  • 录用日期:  2018-08-10
  • 网络出版日期:  2019-01-20

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