利用无人机激光点云数据更新地形级实景三维地理场景

随着实景三维建设工作的不断推进,对地形级实景三维地理场景的现势性要求日益提高。为解决地理场景更新的及时性和高效性,本文利用无人机获取遥感卫星监测变化图斑点云数据,通过TerraScan点云分类滤波算法批量提取地面点,并采用CloudCompare拉普拉斯平滑算法进行平滑处理,进一步消除离散噪点,输出数字高程模型,最终实现地形级实景三维地理场景更新。试验结果表明,该方法自动化程度高,精度可靠,更新后的实景三维地理场景准确、自然真实。

Updating entities for terrain-level 3D real scene by UAV laser point cloud data

With the continuous development of 3D real scene construction, the requirement for the current situation of entities for terrain-level 3D real scene is increasing day by day. In order to solve the timeliness and high efficiency of geographical scene update, this paper uses unmanned aerial vehicle (UAV) to obtain the laser-point cloud data onto the changed pattern monitored by remote sensing satellites. TerraScan point cloud classification filter algorithm is used to extract ground points in batches, and CloudCompare Laplacian smoothing algorithm is used to smooth and eliminate discrete non-ground points. DEM is output, and finally entities for terrain-level 3D real scene update is achieved. The experimental results show that this method is high degree of automation, and reliable in precision. The updated 3D real scene is accurate and natural.