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基于室内标志的视觉定位方法

黄刚 蔡浩 邓超 何志 许宁波

黄刚, 蔡浩, 邓超, 何志, 许宁波. 基于室内标志的视觉定位方法[J]. 交通信息与安全, 2021, 39(6): 172-179. doi: 10.3963/j.jssn.1674-4861.2021.06.020
引用本文: 黄刚, 蔡浩, 邓超, 何志, 许宁波. 基于室内标志的视觉定位方法[J]. 交通信息与安全, 2021, 39(6): 172-179. doi: 10.3963/j.jssn.1674-4861.2021.06.020
HUANG Gang, CAI Hao, DENG Chao, HE Zhi, XU Ningbo. A Visual Localization Method Based on Indoor Signs[J]. Journal of Transport Information and Safety, 2021, 39(6): 172-179. doi: 10.3963/j.jssn.1674-4861.2021.06.020
Citation: HUANG Gang, CAI Hao, DENG Chao, HE Zhi, XU Ningbo. A Visual Localization Method Based on Indoor Signs[J]. Journal of Transport Information and Safety, 2021, 39(6): 172-179. doi: 10.3963/j.jssn.1674-4861.2021.06.020

基于室内标志的视觉定位方法

doi: 10.3963/j.jssn.1674-4861.2021.06.020
基金项目: 

国家自然科学基金青年基金项目 52002298

湖北省自然科学基金青年项目 2020CFB118

湖北省教育厅科学技术研究计划青年人才项目 Q20201107

详细信息
    作者简介:

    黄刚(1989—), 博士, 讲师. 研究方向: 室内定位、智能汽车感知、场景建模. E-mail: ghuang@wust.edu.cn

    通讯作者:

    蔡浩(1989—), 博士, 讲师. 研究方向: 智能交通系统、安全辅助驾驶、智能车定位、驾驶行为分析. E-mail: caihao@wtu.edu.cn

  • 中图分类号: U4

A Visual Localization Method Based on Indoor Signs

  • 摘要: 为解决室内交通场景中智能汽车和移动机器人进行定位计算的问题, 利用室内场景中已存在的各类标志, 引入BEBLID算法, 提出1种视觉定位方法。对BEBLID算法进行改进, 赋予其对图像整体进行特征表征的能力。将定位过程分解为离线阶段和在线阶段, 离线阶段构建场景标志地图。在线阶段中, 首先通过全局特征匹配, 引入KNN方法确定最近节点和最近图像。通过局部特征匹配获得特征点一一对应关系。利用场景特征地图中存储的标志坐标信息, 进行度量计算, 获取当前位置信息。在教学楼、办公楼和室内停车场场景进行实验, 实验中对场景标志的正确识别率达到90%, 平均定位误差小于1 m, 与传统方法相比, 同一样本下识别精度相对提升约10%, 实验验证了算法的有效性。

     

  • 图  1  方法流程图

    Figure  1.  Flow of the proposed method

    图  2  BEBLID全局与局部描述符示例

    Figure  2.  Examples of holistic and local BEBLID features

    图  3  实验场景中部分标志图像

    Figure  3.  Sign images from experiment scenes

    图  4  第一类场景标志识别结果

    Figure  4.  Recognition results of the signs in the class-1 scene

    图  5  局部BEBLID特征匹配效果

    Figure  5.  Matching performance of local BEBLID features

    图  6  第一类场景定位误差

    Figure  6.  Localization error in the class-1 scene

    图  7  第二类场景标志识别结果

    Figure  7.  Recognition results of the signs in the class-2 scene

    图  8  第二类场景定位误差

    Figure  8.  Localization error in the class-2 scene

    表  1  计算效率对比实验结果

    Table  1.   Comparison experiments of calculation efficacy  单位: ms

    方法 场景1 场景2
    本文方法 92.0 95.3
    ORB[15] 92.7 96.4
    下载: 导出CSV

    表  2  第一类场景定位误差

    Table  2.   Localization error in the class-1 scene

    场景 平均误差/m 标准偏差/m 小于1 m的概率/% 耗时/ms
    场景1 0.80 1.38 87 152.0
    场景2 0.82 1.41 87
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-23
  • 网络出版日期:  2022-01-12

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