基于Car(p,q)模型和数学形态学理论的LiDAR点云数据滤波

在分析现有的LiDAR点云数据后处理方法的基础上，本文提出了一种点云数据“分步”滤波方法。首先对LiDAR点云数据进行数学形态学“粗”滤波，得到“地面点假设”和“非地面点假设”。然后引入顾及因果关系的自回归模型（car）对两类点云数据假设进行模型化处理和假设检验，根据假设检验的结果判断地面点和非地面点，最终得到可靠的分类结果。与单纯的“最小二乘拟合预测法”或“数学形态学”方法相比，这种“分步”处理的思想用于LiDAR点云数据分类处理的结果更可靠。

Filtering of Airborne LiDAR Point Cloud Data Based on car（p,q） Model and Mathematical Morphology

Based on the existing post-processing methods of LiDAR data,a "separated step-by-step" filtering method of point cloud is proposed.First,a "rough" filtering method is applied to the LiDAR point cloud and the "ground points hypothesis" and "non-ground points hypothesis" are gained.Then,a causal auto-regressive model(car model) is imported to do modeling of the ground surface and hypothesis test for the two classes of point clouds,and ground points and non-ground points are classified by the results of the hypothesis testing.Finally,the reliable classification results are gained.Compared to the"least squares prediction method"and"mathematical morphology",the results of LiDAR point cloud filtering by the "separated step-by-step" processing method are more reliable.