Volume 45 Issue 7
Jul.  2020
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WANG Peng, LIU Rufei, MA Xinjiang, LU Xiushan, TIAN Maoyi. An Automatic Extraction Method for Pole-Like Objects fromVehicle-Borne Laser Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 1035-1042. DOI: 10.13203/j.whugis20170421
Citation: WANG Peng, LIU Rufei, MA Xinjiang, LU Xiushan, TIAN Maoyi. An Automatic Extraction Method for Pole-Like Objects fromVehicle-Borne Laser Point Cloud[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 1035-1042. DOI: 10.13203/j.whugis20170421
shu

An Automatic Extraction Method for Pole-Like Objects fromVehicle-Borne Laser Point Cloud

  • 1.

    Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China

  • 2.

    Ocean Science and Engineering College, Shandong University of Science and Technology, Qingdao 266590, China

Funds: 

Shandong Provincial Natural Science Foundation ZR2019BD033

the National Key Research and Development Program of China 2016YFB0501705

The National Basic Surveying and Mapping Science and Technology Program of China 2016KJ0101

Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents 2016RCJJ004

More Information
  • Author Bio:

    WANG Peng, master, specializes in mobile laser scanning data processing. E-mail:stncwp@163.com

  • Corresponding author:

    LIU Rufei, PhD, associate professor. E-mail:liurufei@sdust.edu.cn

  • Received Date: July 12, 2018
  • Published Date: July 29, 2020
    Graphical Abstract
    • Abstract
  • The pole-like objects are the impontant infrastructure in cities, and their automatic extraction is important for fast updating of smart city data. The vehicle-borne mobile measurement system can quickly acquire high-precision point cloud data of the objects in both roadsides. An automatic extraction method for pole-like objects from vehicle-borne laser point cloud data based on circular arc characteristic is proposed. Firstly, according to the target distribution morphology in single and adjacent scanning lines, the non-pole column part in original point cloud is filtered by clustering and denoising. Then, the circular arc point set is searched by constraint random sample consensus (RANSAC) operator, and the 3D statistical regularity of the circular arc are used to identify the column part of pole-like object accurately. Finally, according to the spatial morphology of the target point cloud, the condition of region growing is dynamically determined, and the complete point cloud of different pole-like objects is searched. Experimental results show that this method can effectively reduce noise interference of non-pole-like objects nearby in pole-like objects extraction and has better adaptive ability.
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    • References (16)
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