| 噪声背景下基于激光雷达点云数据的分裂导线自动提取与三维重建 |
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| 引用本文：谢洪平,陈兵,杜长青,孙铭泽,王磊磊,生红莹.噪声背景下基于激光雷达点云数据的分裂导线自动提取与三维重建[J].电网与清洁能源,2020,36(4):23~31 |
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| 基金项目:国家电网公司科技攻关项目（SGTYHT 18-JS-206） |
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| 中文摘要:提出了基于改进DPC算法的具有强抗噪性的分裂导线自动提取与三维重建方法。以噪声背景下的输电线路点云数据为分析对象，首先根据特征分析法滤除输电线路点云中的地物点和杆塔点，并采用K-MEANS聚类方法提取每根电力线点云；然后提出改进DPC算法实现分裂子导线点云的聚类和提取；最后采用最小二乘法实现各分裂子导线三维模型重建。并与K-MEANS算法的聚类结果进行了对比，证明了改进DPC算法的强抗噪性和鲁棒性。 |
| 中文关键词:分裂导线 DPC算法改进 抗噪性 点云提取 |
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| Automatic Extraction and 3D Reconstruction of Bundle Conductors Based on Lidar Point Cloud Data in Noise Background |
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| Abstract:A set of anti-noise method based on improved DPC algorithm for automatic extraction and 3D reconstruction of the bundled conductor is proposed in this paper. The point cloud data of power lines under noise background is selected for analysis. First， according to the feature analysis method， the points of ground objects and power towers in the point cloud of the transmission line are filtered out， and each power line’s point cloud is extracted by K-MEANS clustering method. Second， the improved DPC algorithm is proposed to realize the clustering and extraction of the point cloud of the bundled sub-conductor. Finally， the 3D reconstruction of each bundled sub-conductor is realized by the least square method. Compared with the clustering results of the K-MEANS method， the improved DPC algorithm has proved to have strong noise immunity and good robustness. |
| keywords:bundle conductor improved DPC algorithm noise immunity point cloud extraction |
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