Article: Registration method for laser point cloud data of power lines based on local features Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2025 Vol.27 No.2/3/4 pp.379 - 394 Abstract: To improve the signal-to-noise ratio and coincidence rate of data registration results, a local feature-based laser point cloud data registration method for power lines is proposed. Firstly, use Leica ScanStation P40 to collect laser point cloud data of power lines and obtain three-dimensional coordinate information. Then, extract local feature descriptors with low sensitivity to noise from three aspects: local depth, normal deviation angle, and point cloud density. Finally, coarse registration is achieved through random sampling consistency algorithm, followed by fine registration using point to plane IC algorithm to achieve the optimal alignment state. The experiment shows that this method can effectively achieve large-scale point cloud data alignment. After applying this method to complete data registration, the highest signal-to-noise ratio can reach 77.2 dB, and the highest coincidence rate can reach 0.990, indicating the effectiveness of this method. Inderscience Publishers - linking academia, business and industry through research

Title: Registration method for laser point cloud data of power lines based on local features

Authors: Gang Cai; Zhengjing Luo; Zhiqiang Xu; Hui Xiao

Addresses: Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, 410004, China ' Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, 410004, China ' Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, 410004, China ' Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, 410004, China

Abstract: To improve the signal-to-noise ratio and coincidence rate of data registration results, a local feature-based laser point cloud data registration method for power lines is proposed. Firstly, use Leica ScanStation P40 to collect laser point cloud data of power lines and obtain three-dimensional coordinate information. Then, extract local feature descriptors with low sensitivity to noise from three aspects: local depth, normal deviation angle, and point cloud density. Finally, coarse registration is achieved through random sampling consistency algorithm, followed by fine registration using point to plane IC algorithm to achieve the optimal alignment state. The experiment shows that this method can effectively achieve large-scale point cloud data alignment. After applying this method to complete data registration, the highest signal-to-noise ratio can reach 77.2 dB, and the highest coincidence rate can reach 0.990, indicating the effectiveness of this method.

Keywords: power lines; laser point cloud; data registration; local feature descriptor; random sampling consistency algorithm; point to plane IC algorithm.

DOI: 10.1504/IJBIDM.2025.149095

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.379 - 394

Received: 04 Dec 2024
Accepted: 21 Aug 2025
Published online: 13 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Registration method for laser point cloud data of power lines based on local features

Keep up-to-date