测绘通报 ›› 2020, Vol. 0 ›› Issue (3): 87-90.doi: 10.13474/j.cnki.11-2246.2020.0084

• 技术交流 • 上一篇    下一篇

利用车载LiDAR点云数据提取城市道路规则多边形

谢宏全1, 梅雪琴1, 蔡东健2,3, 王亚娜1, 刘付程1   

  1. 1. 江苏海洋大学测绘与海洋信息学院, 江苏 连云港 222005;
    2. 苏州工业园区测绘地理信息有限公司, 江苏 苏州 215000;
    3. 河海大学地球科学与工程学院, 江苏 南京 210098
  • 收稿日期:2019-11-20 修回日期:2020-01-17 出版日期:2020-03-25 发布日期:2020-04-09
  • 作者简介:谢宏全(1964-),男,博士,教授,主要从事三维激光扫描技术应用研究。E-mail:251571789@qq.com
  • 基金资助:
    国家自然科学基金(41976187);江苏省海洋技术品牌专业资助(PPZY2015B116)

Method for extracting urban road regular polygon by using vehicle-mounted LiDAR point cloud data

XIE Hongquan1, MEI Xueqin1, CAI Dongjian2,3, WANG Yana1, LIU Fucheng1   

  1. 1. School of surveying and Mapping and Marine Information, Jiangsu Ocean University, Lianyungang 222005, China;
    2. Suzhou Industrial Park surveying and Mapping Geographic Information Co., Ltd., Suzhou 215000, China;
    3. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
  • Received:2019-11-20 Revised:2020-01-17 Online:2020-03-25 Published:2020-04-09

摘要: 随着自动驾驶高精地图的发展,准确高效地提取道路规则多边形成为必要。本文以苏州市某城市快速路为研究对象,通过使用徕卡车载激光移动测量系统获得城市道路点云数据,观察了解车载点云数据各类属性信息,根据观察结果组合利用点云数据高差、点云灰度差值、点云强度差值,在此基础上再利用网格密度法实现道路规则多边形的提取,然后通过对目标的矢量化得到三维矢量规则多边形。最后对提取的矢量化结果进行精度评定。试验结果表明:该方法在提取道路规则多边形方面能得到较好的结果且能满足高精地图需求。

关键词: 高精地图, 车载点云, 道路规则多边形, 网格密度, 三维矢量

Abstract: With the development of self-driving high-precision map, it is necessary to extract the road rules accurately and efficiently. This paper takes a city expressway in suzhou city as the research object, obtains the city road point cloud data by using the leica vehicle-borne laser mobile measurement system, observes and understands all kinds of attribute information of the vehicle-borne point cloud data. According to the observation results, the point cloud data height difference, the point cloud gray level difference and the point cloud intensity difference are combined. Finally, the accuracy of the extracted vectorization results is evaluated. The experimental results show that: This method can get good results in extracting regular polygons and can meet the demand of high precision maps.

Key words: high precision map, vehicle point cloud, road rule sign marking, mesh density, 3D vector

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