游安清, 潘旭东, 赵平, 潘文武. 激光雷达电力巡线点云自动分类方法研究[J]. 应用光学, 2019, 40(6): 1077-1083. DOI: 10.5768/JAO201940.0602005
引用本文: 游安清, 潘旭东, 赵平, 潘文武. 激光雷达电力巡线点云自动分类方法研究[J]. 应用光学, 2019, 40(6): 1077-1083. DOI: 10.5768/JAO201940.0602005
YOU Anqing, PAN Xudong, ZHAO Ping, PAN Wenwu. Research on automatic classification of point cloud scanned by lidar during power-line patrol[J]. Journal of Applied Optics, 2019, 40(6): 1077-1083. DOI: 10.5768/JAO201940.0602005
Citation: YOU Anqing, PAN Xudong, ZHAO Ping, PAN Wenwu. Research on automatic classification of point cloud scanned by lidar during power-line patrol[J]. Journal of Applied Optics, 2019, 40(6): 1077-1083. DOI: 10.5768/JAO201940.0602005

激光雷达电力巡线点云自动分类方法研究

Research on automatic classification of point cloud scanned by lidar during power-line patrol

  • 摘要: 针对机载激光雷达电力巡线的三维点云,设计一种全自动的电线、电塔和地面点云分类方法,为输电系统净空排查提供必需的前提。方法的核心是充分利用激光点云走向的全局统计特征,最大限度减少对局部特征的依赖,以免造成适用局限性。通过国家电网十几段电力巡线数据的应用测试,显示该方法的自动化程度和普适性都很好,大大减少了以往通过商业软件人工交互式分类的工作量,对同样的数据集和分类结果,处理时间由平均1 h缩短到一两秒钟,大大提高了作业效率和用户体验。

     

    Abstract: For 3D laser point cloud scanned by lidar on power lines, an automatic method was designed to divide point cloud into 3 types: point of lines, point of pylons, and point of ground objects. This process can lay essential foundation for spatial distance check in power transmission system. The core of the method is making full use of global statistical features of point cloud instead of local features, thereby maximally prevented limitation of the algorithm. According to dozens of practical tests from State Grids, the automation and universality of this method are satisfactory, which largely reduce the work usually laborious in traditional classification with commercial softwares by half-automatical interactive process, and shorten the average processing time from about 1 hour to several seconds, greatly improve working efficiency and userexperience.

     

/

返回文章
返回