Error Analysis and Quality Control of Point Cloud Denoising for Power Transmission Lines Oriented to Business Accuracy

Zhou Li; Tao Liming; Yang Shengfeng; Ren Mingyang; Sun Haoran

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Error Analysis and Quality Control of Point Cloud Denoising for Power Transmission Lines Oriented to Business Accuracy

更新时间：2026-04-08

- Error Analysis and Quality Control of Point Cloud Denoising for Power Transmission Lines Oriented to Business Accuracy
- Issue 2, (2026)
- 作者机构：
  
  1. 贵州电网有限责任公司运行分公司
  2. 许继电气股份有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2026
- 稿件说明：
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Zhou Li, Tao Liming, Yang Shengfeng, et al. Error Analysis and Quality Control of Point Cloud Denoising for Power Transmission Lines Oriented to Business Accuracy[J]. 2026, (2).
DOI：

Zhou Li, Tao Liming, Yang Shengfeng, et al. Error Analysis and Quality Control of Point Cloud Denoising for Power Transmission Lines Oriented to Business Accuracy[J]. 2026, (2). DOI：

摘要

针对输电线路机载 LiDAR 点云去噪缺乏客观真值与业务级精度评价的问题，提出一种基于逆向噪声注入的量化评估框架。该方法依据物理成像机理构建光斑发散、多路径伪影等噪声模型生成完备真值，并建立了包含导线径向收敛误差、弧垂漂移及杆塔保真度的特征级评价体系。系统评估表明：半径滤波虽能去噪，但会引入显著的导线径向收缩误差；统计滤波在应对杆塔强反射伪影时失效；移动最小二乘虽保形性较好，但在极端非均匀噪声下，存在不可忽视的中心漂移风险。基于此，该文提出了面向业务的分级自适应处理策略，通过算法组合与参数寻优，有效解决了单一算法难以兼顾导线精度与杆塔拓扑完整的矛盾，为输电数字化巡检数据质量控制提供了科学依据。

Abstract

To address the lack of objective ground truth and operational-level accuracy evaluation standards in the preprocessing of airborne LiDAR point clouds for transmission lines

this paper proposes a quantitative evaluation framework based on reverse noise injection. Based on the physical imaging mechanism

this method constructs mathematical models for typical noises such as beam divergence and multipath artifacts to generate complete ground truth data. Furthermore

a feature-level evaluation system comprising conductor radial convergence error

sag drift

and tower fidelity is established. Systematic evaluation indicates that while Radius Outlier Removal (ROR) effectively removes noise

it introduces significant conductor radial shrinkage error. Statistical Outlier Removal (SOR) fails to handle high-density strong reflection artifacts on towers. Moving Least Squares (MLS) offers superior shape preservation but poses a non-negligible risk of center drift under extreme non-uniform noise conditions .Consequently

an application-oriented hierarchical adaptive processing strategy is proposed. Through algorithm combination and parameter optimization

this strategy effectively resolves the conflict between achieving micrometer-level accuracy for conductors and maintaining topological completeness for towers

providing a scientific basis for data quality control in digital power transmission inspections.

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