无人机摄影测量点云道路自适应提取

在无人机摄影测量中, 针对传统的地面点云提取方法对图像点云数据中的道路提取适应性较差的问题, 本文提出了一种无人机摄影测量点云道路自适应提取方法. 首先, 根据点云的空间几何特征将点云划分为3个类别; 然后, 针对非道路的点云类别采取相应的方法进行剔除; 最后, 对经过自适应提取方法得到的点云数据进行滤波平滑和基于颜色的区域生长分割处理. 实验结果表明, 该方法提取的道路点云的I类误差为4.97%, II类误差为1.14%. 该方法能够有效地提取目标道路路面, 提高了无人机摄影测量工程应用中点云数据处理的效率.

Adaptive Extraction of UAV Photogrammetric Point Cloud Road Surface

In UAV photogrammetry, traditional ground point cloud extraction methods have poor adaptability when extracting roads from image point cloud data. Therefore, this study proposes a UAV photogrammetric point cloud road adaptive extraction method. Firstly, the point cloud is divided into three categories based on its spatial geometric characteristics. Then, corresponding methods are applied to remove non-road point cloud categories. Finally, the point cloud data obtained through the adaptive extraction method is filtered for smoothing and subjected to color-based region growing segmentation. Experimental results show that the I-class error of road point cloud extracted by this method is 4.97%, and the II-class error is 1.14%. This method effectively extracts target road surfaces, improving the efficiency of point cloud data processing in UAV photogrammetric applications.