改进移动曲面最小二乘拟合的Lidar数据滤波优化

Optimization of Lidar Data Filtering with Improved Least Square Fitting of Moving Curve

机载激光雷达已允许快速生成大面积区域的高分辨率数字高程模型,但是自动识别密集建筑物或茂密植被所覆盖区域的地面点与非地面点还比较困难。提出了一种移动曲面拟合最小二乘迭代算法自动快速对Lidar数据进行滤波,该方法采用移动窗口加权迭代最小二乘法来选择种子点,基于自适应阈值,逐步对非地面点和地面点进行滤波和分类。在四个研究区域进行的试验表明,新的滤波方法可以将市区和茂密植被覆盖的地面和非地面点分开。对于Ⅰ类误差,新算法的错误范围是4.08%~9.40%,对于Ⅱ类误差,错误范围是2.48%~7.63%,对于总误差,错误范围是5.01%~7.40%。

Airborne lidar has allowed the rapid generation of high-resolution digital terrain models of large areas, but it is still difficult to automatically identify ground points and non-ground points in areas covered by dense buildings or dense vegetation. This paper proposes a mobile curve fitting least squares iterative algorithm automatically and quickly filters Lidar data. This method uses moving window weighted iterative least squares method to select seed points, and based on adaptive thresholds, it gradually filters and classifies non-ground points and ground points. Experiments in four study areas show that the new filtering method can separate the urban area and the ground and non-ground points covered by dense vegetation. For type Ⅰ errors, the error range of the new algorithm is 4.08% to 9.40%, for type Ⅱ errors, the error range is 2.48% to 7.63%, and for total errors, the error range is 5.01% to 7.40%.