应用科学学报 ›› 2022, Vol. 40 ›› Issue (6): 964-972.doi: 10.3969/j.issn.0255-8297.2022.06.007

• 信号与信息处理 • 上一篇    

一种改进的激光雷达点云公路断面生产方法

郑亮1, 张志艺2, 琚宝林1, 李圣明1   

  1. 1. 中交第二公路勘察设计研究院有限公司, 湖北 武汉 430056;
    2. 湖北省测绘质量监督检验站, 湖北 武汉 430074
  • 收稿日期:2022-01-28 发布日期:2022-12-03
  • 通信作者: 郑亮,教授级高工,研究方向为公路摄影测量与遥感等。E-mail:zhengliang_313@163.com E-mail:zhengliang_313@163.com
  • 基金资助:
    中交第二公路勘察设计研究院有限公司重点科技项目(No.KJFZ-2018-043)资助

An Improved Method for Highway Cross Section Production Using LiDAR Point Cloud

ZHENG Liang1, ZHANG Zhiyi2, JU Baolin1, LI Shengming1   

  1. 1. CCCC Second Highway Consultants Co., Ltd., Wuhan 430056, Hubei, China;
    2. Hubei Quality Supervision and Inspection Station of Surveying and Mapping, Wuhan 430074, Hubei, China
  • Received:2022-01-28 Published:2022-12-03

摘要: 传统点云断面提取是在3D产品基础上进行的,需要先对点云滤波获得地面点,再采样得到断面信息,这一过程处理数据量大、工作效率低而且断面成果质量难以保证,需要大量手工精化工作。该文针对公路断面专题制作的生产需求,提出了一种新的生产流程及方法,首先基于数字表面模型数据提取断面,对断面的点云集合进行二维投影,得到二维点阵列;再基于偏度平衡的滤波算法对点云阵列进行滤波;最后进行保留特征的断面点云抽稀。实验表明,新流程减少了数据处理量,有效提高了断面生产的效率和精度。

关键词: 机载激光雷达, 断面, 点云滤波, 偏度平衡, 点云抽稀

Abstract: Traditional point cloud section extraction is carried out on the basis of 3D products. It is necessary to obtain ground points by filtering point cloud firstly and then sampling to obtain sections. The process suffers low work efficiency and unguaranteed quality of the section production, requiring a lot of manual refinement work. This paper proposes a new production process and method for the production demand of highway cross section thematic production. Firstly, a cross section is extracted based on digital surface model (DSM), and the point cloud set of the cross section is two-dimensionally projected to obtain a two-dimensional point array. Then, the point cloud array is filtered based on the skewness-balanced filtering algorithm, and finally a feature preserving cross section point cloud is extracted. Practice has proved that the new process reduces the amount of data processing and effectively improves the efficiency and precision of cross section production.

Key words: airborne LiDAR, cross section, point cloud filtering, skewness balance, point cloud thinning

中图分类号: