Transmission line tower tilt detection under flooding conditions based on UAV LiDAR

doi:10.13474/j.cnki.11-2246.2026.0811

Abstract

Abstract: [Purposes] To address the challenges of invisible tower bases and enhanced point cloud noise in flood-submerged environments，a novel UAV LiDAR-based method is proposed for detecting transmission tower inclination in this paper. [Methods] This method requires only a horizontal member cross-section at the tower bottom and the tower top position to complete inclination assessment，eliminating the need for tower base references and complete tower body data.First，the cross-sectional point cloud of horizontal members is extracted using a bounding box and denoised through statistical filtering.Then，a multi-iteration RANSAC combined with PCA strategy is employed to fit the cross-sectional plane，and a convex hull-rotating calipers-circumscribed square method is used to extract the accurate section center.Finally，the tilt rate is calculated by combining the tower top position with the tower height.An axial-rotation directional graded tilt simulation method is proposed to validate the algorithm performance. [Findings] Results show that within a 6.0‰ tilt rate range，the horizontal offset detection error is less than 6.0 mm，the tilt angle error is below 0.01°，and the tilt rate error is under 0.2‰，with all correlation coefficients exceeding 0.99，meeting the requirements of transmission line operation regulations. [Conclusions] This method provides an effective technical approach for tower inspection and post-disaster assessment in complex aquatic environments．

Key words: UAV LiDAR, transmission tower, tilt detection, flooding conditions, point cloud processing

CLC Number:

P237

Wei Pengcheng, Wang Xiulong, Wang Yuyang, Huang Haifeng, Guo Ruolan, Guo Fei, Liu Yi, Zhao Binbin, Wen Qingfeng, An Yang, Guo Wei. Transmission line tower tilt detection under flooding conditions based on UAV LiDAR[J]. Bulletin of Surveying and Mapping, 2026, 0(8): 74-81.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.0811

http://tb.chinasmp.com/EN/Y2026/V0/I8/74

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