基于PTD和改进曲面拟合的高山区水电工程机载激光雷达点云滤波方法

针对高山区水电工程区域地形起伏大、植被茂密且存在部分建筑物,在提取这些区域地形时机载LiDAR点云滤波方法存在精度不高的问题,提出了一种综合点云回波特性、渐进三角网加密(PTD)算法、改进的曲面拟合算法和地面点精细化处理的山地点云滤波方法。该方法在对机载激光雷达(LiDAR)点云数据去噪的基础上,先利用植被点云的回波特性去除部分植被点,再使用PTD算法进行两次迭代计算获取部分地面点集合,然后将得到的部分地面点集合作为改进的曲面拟合算法的种子点进行格网区域化的曲面拟合来获取原始点云数据中的地面点,最后通过点云的精细化处理去除地面点中夹杂的低矮植被点,以此获得最终的地面点集合。选取DJI-M600搭载HS-600的机载LiDAR测量系统实测数据进行试验,并将提出的滤波方法与PTD算法、曲面拟合滤波算法、区域生长滤波算法和形态学滤波算法进行横向对比,通过点云误分率对滤波方法进行评价。结果表明,与其他4种方法相比,基于PTD和改进曲面拟合的滤波算法的第Ⅰ类误差、第Ⅱ类误差和总误差的最大减小幅度分别为6.25%、1.82%和2.93%,更适用于高山区水电工程机载LiDAR点云数据的滤波处理。

Airborne LiDAR point cloud filtering method for high mountain hydropower projects based on PTD and modified surface fitting

Due to the large terrain fluctuations, dense vegetation and some buildings for hydropower projects in high mountain areas, the problem of low accuracy of airborne LiDAR point cloud filtering always exists. A point cloud filtering method that includes the point cloud echo characteristic, a progressive triangulated irregular network densification(PTD)algorithm, an improved surface fitting algorithm and a refined ground point cloud filtering method was proposed. Based on the denoising of the airborne LiDAR point cloud data, the echo characteristics of the vegetation point cloud is first used to remove some vegetation points, and the PTD algorithm is applied to perform two iterative calculations to obtain a partial ground point set which is used as the seed point of the improved surface fitting algorithm to perform surface fitting of the grid area to obtain the ground points in the original point cloud data. Finally, the point cloud is refined to remove the low-level vegetation points included in the ground points to get the final ground points set. The measured data of the DJI-M600 equipped with the HS-600 airborne LiDAR measurement system was selected for experiments, and the proposed filtering method was compared with the PTD algorithm, surface fitting filtering algorithm, area growth filtering algorithm and morphological filtering algorithm. The results show that compared with the other four methods, the maximum reductions of the first type error, the second type error, and the total error are 6. 25%, 1. 82%, and 2. 93%, respectively. The proposed method is more suitable for the filtering process of airborne LiDAR point cloud data of hydropower projects in high mountainous areas.