Article: Quality inspection of power transmission towers based on point cloud registration Journal: International Journal of Energy Technology and Policy (IJETP) 2025 Vol.20 No.7 pp.3 - 22 Abstract: To address the inefficiency in feature point extraction and registration caused by the complex structure of power transmission towers, this study proposes a feature registration strategy incorporating curvature feature. Given the critical role of power transmission infrastructure in smart grid systems, accurate and efficient tower modelling is essential for ensuring structural safety and operational reliability. First, an algorithm based on normal vector angles is employed to obtain an initial set of feature points. Subsequently, high-curvature points rich in geometric information are identified and retained through Gaussian curvature analysis. This approach enhances feature distinctiveness compared to uniform sampling or intensity-based selection methods. To further enhance registration efficiency, Gaussian curvature parameters are introduced into the random sample consensus (RANSAC) algorithm for preliminary matching. This integration significantly reduces the number of incorrect correspondences compared to standard RANSAC implementations. Additionally, a symmetric objective function optimises the iterative closest point (ICP) algorithm to achieve precise registration across surfaces with varying characteristics. Unlike conventional ICP, which assumes consistent surface normals, the proposed method handles asymmetric structures more effectively. Finally, by using 3D inspection software to compare the registered point cloud with a standard model, accurate quality assessment data for power transmission towers are obtained. Inderscience Publishers - linking academia, business and industry through research

Title: Quality inspection of power transmission towers based on point cloud registration

Authors: Xuan Qi; Honglin Yan; Xiao Tu; Yinying Liu; Weihua Ding

Addresses: Construction Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, Jiangsu, China ' Construction Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, Jiangsu, China ' Construction Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, Jiangsu, China ' Construction Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, Jiangsu, China ' Construction Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, Jiangsu, China

Abstract: To address the inefficiency in feature point extraction and registration caused by the complex structure of power transmission towers, this study proposes a feature registration strategy incorporating curvature feature. Given the critical role of power transmission infrastructure in smart grid systems, accurate and efficient tower modelling is essential for ensuring structural safety and operational reliability. First, an algorithm based on normal vector angles is employed to obtain an initial set of feature points. Subsequently, high-curvature points rich in geometric information are identified and retained through Gaussian curvature analysis. This approach enhances feature distinctiveness compared to uniform sampling or intensity-based selection methods. To further enhance registration efficiency, Gaussian curvature parameters are introduced into the random sample consensus (RANSAC) algorithm for preliminary matching. This integration significantly reduces the number of incorrect correspondences compared to standard RANSAC implementations. Additionally, a symmetric objective function optimises the iterative closest point (ICP) algorithm to achieve precise registration across surfaces with varying characteristics. Unlike conventional ICP, which assumes consistent surface normals, the proposed method handles asymmetric structures more effectively. Finally, by using 3D inspection software to compare the registered point cloud with a standard model, accurate quality assessment data for power transmission towers are obtained.

Keywords: iterative closest point; ICP; power transmission; Gaussian curvature; random sample consensus; RANSAC; 3D inspection.

DOI: 10.1504/IJETP.2025.151788

International Journal of Energy Technology and Policy, 2025 Vol.20 No.7, pp.3 - 22

Received: 26 Dec 2024
Accepted: 25 Jul 2025
Published online: 19 Feb 2026 *

Title: Quality inspection of power transmission towers based on point cloud registration

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