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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.
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.
ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds[J]. Journal of Transport Information and Safety.
Citation: ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds[J]. Journal of Transport Information and Safety.

基于3D点云语义地图表征的智能车定位

基金项目: 

国家重点研发计划项目(2018YFB1600801)、武汉市科技局项目(2020010601012165,2020010602011973,2020010602012003)、重庆市自然科学基金项目(cstc2020jcyj-msxmX0978)资助

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

Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds

  • 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: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;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;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。

     

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出版历程
  • 收稿日期:  2021-10-14
  • 网络出版日期:  2021-12-14

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