平原地区机载激光雷达数据的抽稀算法分析

目前，机载激光雷达点云数据在测绘行业中的应用还存在较多的瓶颈。为了使机载激光雷达点云数据更好地服务等值线等数据的生产，发挥其高效和高精度的优势，本文归纳、总结了国内外现有的LiDAR点云数据抽稀算法，并通过对比分析现有LiDAR点云数据抽稀算法存在的优缺点，如系统抽稀、格网抽稀、TIN抽稀和坡度抽稀等算法，结合平原地区激光点云在实际生产中的应用，研究了更适合平原地区点云数据的抽稀方法，通过大量的数据测试和试生产。结果表明，该方法可以在应用项目精度约束下保证数据质量，减少了后期数据处理应用的难度，提升了后续成果数据的质量，提高了作业生产效率，对机载激光雷达点云数据在测绘行业中的应用推广具有重要的现实意义。

Study and application on thinning algorithm of airborne LiDAR data in plain area

At present, there are still many bottlenecks in the application of airborne LiDAR point cloud data in the surveying and mapping industry. In order to make airborne LiDAR point cloud data better serve the production of equivalent data, and give play to its advantages of high efficiency and high precision. In this paper, we summarize the existing algorithm of cloud data thinning in LiDAR at home and abroad, and analyze the advantages and disadvantages of the existing algorithm, such as system thinning, grid thinning, TIN thinning and slope thinning. Based on the application of laser point cloud in actual production in plain area, the method of thinning data more suitable for plain area is studied. Through a lot of data testing and trial production, the results show that this method can guarantee data quality under the constraints of application project accuracy. It reduces the difficulty of late data processing, improves the quality of the follow-up data, and improves the efficiency of the operation. It has a very important practical significance for the application and promotion of airborne Lidar cloud data in the surveying and mapping industry.