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基于3D点云语义地图表征的智能车定位

朱云涛 李飞 胡钊政 吴华伟

朱云涛, 李飞, 胡钊政, 吴华伟. 基于3D点云语义地图表征的智能车定位[J]. 交通信息与安全, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017
引用本文: 朱云涛, 李飞, 胡钊政, 吴华伟. 基于3D点云语义地图表征的智能车定位[J]. 交通信息与安全, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017
ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points[J]. Journal of Transport Information and Safety, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017
Citation: ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points[J]. Journal of Transport Information and Safety, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017

基于3D点云语义地图表征的智能车定位

doi: 10.3963/j.jssn.1674-4861.2021.06.017
基金项目: 

国家重点研发计划项目 2018YFB1600801

武汉市科技局项目 2020010601012165

武汉市科技局项目 2020010602011973

武汉市科技局项目 2020010602012003

重庆市自然科学基金项目 cstc2020jcyj-msxmX0978

详细信息
    作者简介:

    朱云涛(1997—), 硕士研究生. 研究方向: 计算机视觉、激光SLAM. E-mail: zyt941292303@whut.edu.cn

    通讯作者:

    胡钊政(1979—), 博士, 教授. 研究方向: 计算机视觉、智能车路协同. E-mail: zzhu@whut.edu.cn

  • 中图分类号: U495

A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points

  • 摘要: 为提高智能车节点定位准确率, 研究了基于3D点云语义地图表征的智能车定位方法。该方法分为3个部分: ①基于三维激光点云的语义分割, 包括地面分割, 交通标志牌分割和杆状语义目标分割; ②面向智能车的点云语义地图表征, 利用分割的语义目标投影, 生成带权有向图, 语义路, 语义编码, 再以语义编码和高精度GPS的全局位置组成语义地图表征模型; ③基于语义表征模型的智能车定位, 包括基于GPS匹配的粗定位和基于语义编码渐进匹配的节点定位。实验在3种长度不同、复杂度不同的道路场景下进行, 节点定位准确率分别为98.5%, 97.6%和97.8%, 结果表明所提出的定位方法节点定位准确率高、鲁棒性强且适用于不同的道路场景。

     

  • 图  1  系统流程图

    Figure  1.  Flow of the system

    图  2  俯仰角评估示意图

    Figure  2.  Schematic of pitch angle evaluation

    图  3  交通标志牌粗分割

    Figure  3.  Coarse segmentation of traffic signs

    图  4  交通标志牌精分割

    Figure  4.  Precise segmentation of traffic signs

    图  5  切片生长法分割

    Figure  5.  Segmentation of slice growth

    图  6  点云语义地图模型

    Figure  6.  Semantic map modeling from point clouds

    图  7  语义俯视投影图

    Figure  7.  Semantic overhead projection

    图  8  语义带权有向图

    Figure  8.  Semantically weighted digraph

    图  9  场景语义编码

    Figure  9.  Scene semantic coding

    图  10  GPS粗定位

    Figure  10.  GPS coarse positioning

    图  11  节点级定位

    Figure  11.  Node-level Localization

    图  12  实验平台和数据集路线

    Figure  12.  Experimental platform and data set route

    图  13  节点的地面分割结果

    Figure  13.  Ground segmentation result of a node

    图  14  节点的交通标志牌分割结果

    Figure  14.  Traffic-sign segmentation of a node

    图  15  节点的杆状语义分割结果

    Figure  15.  Pole-shaped object segmentation result of a node

    图  16  地图节点全局位置轨迹

    Figure  16.  Global positional trajectory of map nodes

    表  1  数据集1~3交通标志牌语义分割对比

    Table  1.   Comparative experiment results of data set 1~3

    数据集 方法 TPs/个 FPs/个 FNs/个 precisions/% completes/%
    1 本文 25 1 1 96.2 96.2
    文献[17] 24 1 2 96.0 92.3
    2 本文 40 1 2 97.6 95.2
    文献[17] 39 2 3 95.1 92.9
    3 本文 54 4 3 92.9 94.7
    文献[17] 52 5 5 91.2 91.2
    下载: 导出CSV

    表  2  数据集1~3杆状语义分割对比

    Table  2.   Comparative experiment results of data set 1~3

    数据集 方法 TPp/个 FPp/个 FNp/个 precisionp/% completep/%
    1 本文 127 3 5 97.7 96.2
    文献[18] 120 4 12 96.8 90.1
    2 本文 202 5 11 97.6 94.8
    文献[18] 187 2 26 95.9 87.8
    3 本文 310 11 16 96.6 96.2
    文献[18] 283 17 43 94.3 86.8
    下载: 导出CSV

    表  3  数据集1~3定位精度对比

    Table  3.   Comparative experiment results of data set 1~3

    数据集 方法 正确个数 错误个数 准确率/%
    1 本文 578 9 98.5
    文献[19] 431 156 73.4
    2 本文 969 24 97.6
    文献[19] 669 324 67.4
    3 本文 1504 34 97.8
    文献[19] 1101 437 71.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-14
  • 网络出版日期:  2022-01-12

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