风电场点云数据分类方法研究

Research on Classification Method of Wind Farm Point Cloud Data

为实现风电设备运输的场景建模与路线优化,研究基于无人机LiDAR数据的点云高精度分类方法。首先采用不规则三角网的渐进加密滤波算法,在原始点云数据中分类出地面点和非地面点,然后基于KANN-DBSCAN算法和SouthLiDAR软件平台分别对非地面点、地面点进行精分类。以广东翁源县风电场建设项目为依托,对无人机采集的点云数据进行分类实验,验证方法的有效性,为后续风电场建模等提供数据支持。

In order to realize scene modeling and route optimization of wind power equipment transportation, a high-precision point cloud classification method based on UAV LiDAR data was studied. Firstly, the ground points and non-ground points were classified from the original point cloud data by using the progressive encryption and filtering algorithm of irregular triangulation network. Then, the non-ground points and non-ground points were accurately classified based on KANN-DBSCAN algorithm and SouthLiDAR software platform respectively. Based on the wind farm construction project in Wengyuan County, Guangdong Province,the point cloud data collected by UAV are classified and tested to verify the effectiveness of the method and provide data support for subsequent wind farm modeling.