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针对车载LiDAR点云进行地面点滤波时,基于常规TIN、坡度等滤波算法不能根据局部地形变化自动调整阈值的问题,该文结合城市点云特征和地形起伏度,提出地形自适应的车载LiDAR点云滤波方法。该方法通过引入地形自适应参数进行区域增长阈值的动态调整,实现地面点、非地面点的自动精确滤波。通过实测数据试验,结果表明该方法可适用于车载LiDAR城市点云中地面点和非地面点的较精确分类,解决低矮浅丘、低矮灌木等地物点不容易正确分类的问题。
In order to solve the problem that the filter algorithm such as conventional TIN and slope can not automatically adjust the threshold according to the local topographic changes, this paper proposes a topographic adaptive LiDAR vehicle point Cloud filtering method. The method adjusts the regional growth threshold dynamically by introducing the terrain adaptive parameters to realize the automatic accurate filtering of the ground point and non-ground point. The experimental results show that the proposed method can be applied to the more accurate classification of ground points and non-ground points in the on-board LiDAR urban vehicle cloud, and solve the problem that it is not easy to correctly classify the low-lying shallow hills and low shrubs.