基于机载点云的电力线提取方法研究

Power Line Extraction Based on Airborne LiDAR Point Clouds

为了提高电力线走廊巡检的有效性,提出一种基于机载点云的电力线提取方法。首先利用渐进三角网滤波剔除地面点,并计算距地高差,得到含电力线点集,为了克服传统二维密度分割方法无法有效识别电力线下方存在地物点的不足,通过三维格网划分密度分割方法,实现电力线点粗提取;然后通过RANSAC方法直线检测与点云高差并判断剔除残留电塔点与绝缘子点,实现电力线点云精提取;此外,借助RANSAC方法曲线检测对同一垂直面上的电力线分股,最后利用K-Means算法完成分裂导线束间归类。实验表明,该电力线点云提取方法正确率达97%以上,非电力线点云误判率低至1%左右,完全满足实际工程需求。

In order to improve the effectiveness of power line corridor inspection, a power line extraction method based on airborne point cloud was proposed. Firstly, the gradual triangular network filtering was used to eliminate the ground points, and the height difference from the ground was calculated to obtain a set of points containing power lines. In order to overcome the shortcomings of traditional two-dimensional density segmentation methods that cannot effectively identify the ground object points below the power line, three-dimensional grid division density segmentation method was adopted to achieve rough extraction of power line points. Through the RANSAC method, the line detection and point cloud height difference were used to analyze and eliminate the residual tower points and insulator points, so as to achieve the precise extraction of power line point clouds. In addition, the power lines on the same vertical plane were split by the curve detection of RANSAC method, and finally the K-Means algorithm was used to complete the extraction between the split wire bundles. The experiment showed that the accuracy of this method for extracting power line point clouds was more than 97%, and the misjudgment rate of non-power line point clouds was as low as about 1%, which can fully meet the needs of practical projects.