| 电力巡线LiDAR点云电塔自动定位和提取算法 |
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| 引用本文： | 黄陆君,陈光平,袁帅,涂朴,乔杰.电力巡线LiDAR点云电塔自动定位和提取算法[J].测绘通报,2020,0(4):53. |
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| 作者姓名： | 黄陆君 陈光平 袁帅 涂朴 乔杰 |
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| 作者单位： | 1. 四川文理学院智能制造学院, 四川 达州 635000;2. 沈阳建筑大学信息与控制工程学院, 辽宁 沈阳 110168;3. 西南交通大学信息科学与技术学院, 四川 成都 611756 |
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| 基金项目： | 国家自然科学青年基金（61703288）；辽宁省自然科学基金面上项目（20180520037）；四川文理学院智能制造产业技术开发研究专项（2019PT004Y；2017ZZ001Y） |
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| 摘 要： | 针对电力巡线机载激光雷达（LiDAR）激光点云电塔自动提取问题，提出了一种电塔自动定位和点云提取算法。首先，基于点云进行二维空间网格划分，利用网格点云高程偏差和方差特征提取潜在电塔网格；其次，基于电塔点云的高程连续特性完成电塔自动定位和点云粗提取；然后，利用点云分层密度信息和图像开运算，实现电塔精细提取；最后，利用轻小型无人机载激光雷达数据验证本文算法的有效性。试验结果表明，本文所提出的自动提取算法，能够有效解决LiDAR数据中电塔自动定位和点云提取问题，在LiDAR数据质量较差时仍能够取得良好效果，算法对于噪点数据具有较强的稳健性。本文所提出的电塔自动提取算法在LiDAR电力巡检数据处理中具有一定的应用价值。
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| 关 键 词： | 电力巡线 LiDAR 点云自动分类 电塔定位 电塔提取 |
| 收稿时间： | 2019-06-07 |
Algorithm of pylon automatically localization and point cloud extraction in power line inspection based on LiDAR |
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| HUANG Lujun,CHEN Guangping,YUAN Shuai,TU Pu,QIAO Jie.Algorithm of pylon automatically localization and point cloud extraction in power line inspection based on LiDAR[J].Bulletin of Surveying and Mapping,2020,0(4):53. |
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| Authors: | HUANG Lujun CHEN Guangping YUAN Shuai TU Pu QIAO Jie |
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| Institution: | 1. School of Intelligent Manufacturing, Sichuan University of Arts and Sciences, Dazhou 635000, China;2. Faculty of Information&Control Engineering Shenyang Jianzhu University, Shenyang 110168, China;3. School of Information Science and technology, Southwest Jiaotong University, Chengdu 611756, China |
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| Abstract: | An automatic pylon localization and point cloud extraction algorithm is presented in this paper in order to solve the problem of pylon automatically extraction from point cloud of power line inspection. Firstly, 2D mesh is applied to get bias and variance of height of point cloud in each grid, and in which pylon is located cursorily. Secondly, points from tower are extracted roughly according to continuity of height in grids. Afterwards, points from tower are accurately extracted based on the structural features of pylon and power line, in which the density in different hierarchy of height and open operation of image are applied. Finally, point cloud collected from light small UAV-LiDAR is applied in experiments for experimental verification. Experiments show that the presented algorithm works well in the problem of tower extraction, and also has a high performance under condition of poor quality of point cloud. Algorithm presented in this paper has some value in application of power line inspection with LiDAR. |
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| Keywords: | powerline inspection LiDAR point cloud classification pylon localization pylon extraction |
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