基于激光点云实现杆塔提取的轻量级网络

A lightweight network for power tower extraction from laser point cloud

为了解决传统的杆塔点云提取算法对地面起伏较为敏感,以及提取出的杆塔点云难以剔除地面点的问题,提出了一种直接以原始点云数据为输入来实现杆塔提取的轻量级网络。将原始点云数据划分为若干大小相等的体素格,利用特征学习网络及卷积网络提取体素格内的空间、结构特征;并结合传统方法中的相对高度差以及点密度的特征,从而判别该体素格为杆塔点云或非杆塔点云;最后采用聚类算法剔除孤立的体素格以提高杆塔点云提取的准确率,得到杆塔的激光点云数据。结果表明,所提出的方法对于不同地形以及不同干扰因素情况下的杆塔,提取精度能达到95%左右。该算法能有效地提取杆塔点云,相对于格网法,其稳定性及精确度有一定提升,且对于高大树木、垂直遮挡等其它因素也有较好的抗干扰效果。

For the problem that the traditional algorithm for the extraction of power tower is sensitive to ground fluctuations,and some points of ground are hard to exclude from the extraction result,a lightweight neural network was proposed with a direct input of the original point cloud data to implement the extraction of power tower.By dividing the original point cloud data into a number of voxel grids of equal size,the feature learning network and a convolutional neural network were used to extract the spatial and structural information in the voxel grid.Then the characteristics of relative height and point density which can be found in traditional algorithms were added to the feature to determine whether the voxel is a type of tower point cloud or not.Finally,clustering was used to eliminate isolated voxels to improve the accuracy and obtain the laser point cloud data of power towers.The experimental results show that the algorithm has an accuracy of about 95%for different terrains and interferences in the extraction of power towers.The algorithm can effectively extract the point cloud of power towers,and has a certain improvement in stability and accuracy compared with the method using grid or other methods,and also has a good anti-interference effect on other factors such as the existence of tall trees or vertical occlusion.