Airborne LIDAR point cloud tower inclination judgment

被引:1
|
作者
Chen Liang [1 ,2 ]
Liu Zhengjun [1 ]
Qian Jianguo [2 ]
机构
[1] Chinese Acad & Surveying & Mapping, Beijing, Peoples R China
[2] Liaoning Tech Univ, Fuxin, Peoples R China
来源
6TH DIGITAL EARTH SUMMIT | 2016年 / 46卷
关键词
D O I
10.1088/1755-1315/46/1/012013
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Inclined transmission line towers for the safe operation of the line caused a great threat, how to effectively, quickly and accurately perform inclined judgment tower of power supply company safety and security of supply has played a key role. In recent years, with the development of unmanned aerial vehicles, unmanned aerial vehicles equipped with a laser scanner, GPS, inertial navigation is one of the high-precision 3D Remote Sensing System in the electricity sector more and more. By airborne radar scan point cloud to visually show the whole picture of the three-dimensional spatial information of the power line corridors, such as the line facilities and equipment, terrain and trees. Currently, LIDAR point cloud research in the field has not yet formed an algorithm to determine tower inclination, the paper through the existing power line corridor on the tower base extraction, through their own tower shape characteristic analysis, a vertical stratification the method of combining convex hull algorithm for point cloud tower scarce two cases using two different methods for the tower was Inclined to judge, and the results with high reliability.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Automatic Power Line Extraction Method Based on Airborne LiDAR Point Cloud Data
    Yang Ye
    Li Hongning
    LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (09)
  • [42] Filtering of Airborne LiDAR Point Cloud with a Method Based on Kernel Density Estimation (KDE)
    Tian, X-R.
    Xu, L-J.
    Li, X-L.
    Xu, T.
    Yao, J-N.
    LASERS IN ENGINEERING, 2016, 34 (4-6) : 221 - 237
  • [43] Fusion of airborne LiDAR point cloud and imagery captured from integrated sensor system
    Hu, Xiangyun
    Ye, Lizhi
    Li, Xiaokai
    Zhu, Junfeng
    Long, Huaping
    INTERNATIONAL SYMPOSIUM ON LIDAR AND RADAR MAPPING 2011: TECHNOLOGIES AND APPLICATIONS, 2011, 8286
  • [44] Algorithm for Extracting Building Roof Surfaces from Airborne LiDAR Point Cloud Data
    Li, Haiwang
    Zhou, Hengke
    Zhao, Xing
    Guo, Cailing
    Li, Bailin
    Computer Engineering and Applications, 2024, 60 (11) : 233 - 241
  • [45] An Ecological Irrigation Canal Extraction Algorithm Based on Airborne Lidar Point Cloud Data
    Wang, Guangqi
    Han, Yu
    Chen, Jian
    Pan, Yue
    Cao, Yi
    Meng, Hao
    Du, Nannan
    Zheng, Yongjun
    INTELLIGENT TECHNOLOGIES AND APPLICATIONS, INTAP 2018, 2019, 932 : 538 - 547
  • [46] An Approach to DSM Refinement with Fusion of Airborne LiDAR Point Cloud Data and Optical Imagery
    Hao Xiangyang
    Zhang Weiqiang
    Jiang Lixing
    MULTISENSOR, MULTISOURCE INFORMATION FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS 2013, 2013, 8756
  • [47] Building contour extraction from Airborne LiDAR point cloud for Digital Line Graphic
    Kan, Yuhui
    Zhang, Tonggang
    Zhong, Dan
    Jia, Shiqiang
    Xie, Fugui
    SPIE FUTURE SENSING TECHNOLOGIES (2020), 2020, 11525
  • [48] Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach
    Liao, Zeyuan
    Dong, Xiujun
    He, Qiulin
    REMOTE SENSING, 2024, 16 (23)
  • [49] Multilevel intuitive attention neural network for airborne LiDAR point cloud semantic segmentation
    Wang, Ziyang
    Chen, Hui
    Liu, Jing
    Qin, Jiarui
    Sheng, Yehua
    Yang, Lin
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 132
  • [50] Finite Element Modelling of a Transmission Steel Lattice Tower Based on LiDAR Point Cloud Data
    Wrzosek, Filip
    ce/papers, 2023, 6 (3-4) : 1174 - 1178