测绘通报 ›› 2022, Vol. 0 ›› Issue (1): 155-158.doi: 10.13474/j.cnki.11-2246.2022.0028

• 技术交流 • 上一篇    下一篇

高精道路地图制作方法及关键技术

徐杰1,2, 侯飞1, 曹广航1   

  1. 1. 山东省国土测绘院, 山东 济南 250101;
    2. 山东科技大学, 山东 青岛 266590
  • 收稿日期:2021-01-18 修回日期:2021-05-25 发布日期:2022-02-22
  • 通讯作者: 侯飞。E-mail:xigehongdan@163.com
  • 作者简介:徐杰(1983-),男,博士,高级工程师,主要研究方向为遥感数据处理及应用。E-mail:86887230@qq.com
  • 基金资助:
    山东省重大科技创新工程(2019JZZY020103)

High-precision road map making method and key technology

XU Jie1,2, HOU Fei1, CAO Guanghang1   

  1. 1. Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250101, China;
    2. Shandong University of Science and Technology, Qingdao 266590, China
  • Received:2021-01-18 Revised:2021-05-25 Published:2022-02-22

摘要: 本文提出一种基于车载移动测量、倾斜摄影等多源数据成果的高精道路地图互补采集方法,并对其关键技术进行了深入研究。首先以国内第一条基于自动驾驶的智能网联高速公路测试路段——淄博智能网联测试基地为例,开展了激光点云、全景照片、倾斜影像等多源数据获取及高精道路地图要素采集。然后以道路三维矢量要素自动提取为基础,辅以人工采编,并在数据差分解算中测试了我国北斗数据。实例表明,本文方法切实可行、精度可靠,有效改善了高精道路地图采集模式,降低了劳动强度,保障了人身安全,提高了作业效率,对于高精道路地图制作具有借鉴价值。

关键词: 车载移动测量, 倾斜摄影, 多源数据, 高精道路地图, 自动提取

Abstract: This paper describes a high-precision road map acquisition method based on multi-source data such as vehicle-borne mobile mapping system and oblique photogrammetry system, and makes in-depth research on key technologies. Zibo intelligent network test base, the first test section of intelligent network expressway based on automatic driving in China, is taken as an example to carry out multi-source data acquisition such as laser point cloud, panoramic photo, tilt image and high-precision road map elements acquisition. This method is based on the automatic extraction of road 3D vector elements, supplemented by manual collection and compilation, and BeiDou data is tested in the data difference calculation. The example shows that this method is feasible and reliable, effectively improves the high-precision road map acquisition mode, reduces the labor intensity, ensures personal safety, and improves the operation efficiency, which is of great reference value for the production of high-precision road map.

Key words: vehicle-borne mobile mapping, oblique photogrammetry, multi-source data, high-precision road map, automatic extraction

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