大型无人机电力巡检LiDAR点云安全距离诊断方法

发布时间：2018-04-28 05:03

本文选题：无人机 + LiDAR　；参考：《电网技术》2017年08期

【摘要】：输电线路走廊中地物安全距离检测是电力运维部门日常线路巡检作业中的重要一环。结合此项运维实际需求,提出一种无人机电力巡检LiDAR点云数据的自动安全距离诊断方法:首先依据已知的线路位置与走向对无人机巡检系统采集的LiDAR点云进行裁剪,获得线路走廊区域点云;其次,采用自适应分区滤波的方法滤除地面点,获得非地面点云;在此基础上,在非地面点数据中,依据点云维数特征以及空间几何分布特性从非地面点中分离出导线点、杆塔点、及林木植被、建筑物等安全距离诊断所关注的线路走廊其他地物;继而提出一种迭代最小二乘电力线悬链线模型解算方法,自无序电力线激光点云数据拟合悬链线方程,用于后续安全距离计算;最后使用分段剖面安全距离计算方法计算线路与线路走廊下方地面/地物距离,并与标准安全距离做比较,对于距离小于安全距离的区域进行危险预警。采用大型无人机电力线路巡检系统采集的多组LiDAR点云数据对文中算法进行实验验证,并对检测结果与人工点云量测值与实地巡检值进行了定性与定量的对比分析。实验结果表明提出的安全距离诊断方法能准确探测树障等安全距离超限地物。
[Abstract]:The detection of the safety distance of ground objects in the transmission line corridor is an important part of the routine line inspection in the power operation and maintenance department. According to the actual requirement of the operation and maintenance, a method of automatic safe distance diagnosis of LiDAR point cloud data for UAV power patrol inspection is proposed. Firstly, according to the known line position and direction, the LiDAR point cloud collected by UAV patrol inspection system is clipped. The point cloud in the corridor area is obtained; secondly, the ground point is filtered by adaptive partitioning filter to obtain the non-ground point cloud; on this basis, in the non-ground point data, According to the characteristics of point cloud dimension and spatial geometric distribution, other features of route corridor which are concerned about the safety distance diagnosis of forest vegetation and buildings are separated from non-ground points, such as traverse points, pole and tower points, and forest vegetation, buildings and so on. Then, an iterative least-squares power line catenary model is proposed. The self-disordered power line laser point cloud data fit the catenary equation for the subsequent calculation of safety distance. Finally, the distance between the ground and ground objects under the route and the corridor is calculated by using the method of section section safe distance calculation, and compared with the standard safety distance, the danger warning is carried out for the area where the distance is less than the safe distance. In this paper, the algorithm is experimentally verified by using the multi-group LiDAR point cloud data collected by the power line inspection system of large UAV, and the qualitative and quantitative comparison between the detection result and the measured value of artificial point cloud and the field inspection value is carried out. The experimental results show that the proposed safe distance diagnosis method can accurately detect the safety distance of ground objects such as tree barriers.
【作者单位】：测绘遥感信息工程国家重点实验室(武汉大学);广东电力科学研究院;时空数据智能获取技术与应用教育部工程研究中心(武汉大学);
【基金】：中国博士后科学基金(2016M600614) 国家自然科学基金重点项目(41531177,41371431)~~
【分类号】：TM755

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