Denoising Algorithm of Airborne LIDAR Point Cloud Based on 3D Grid

Abstract

The first step of the airborne laser lidar point cloud processing is the removal of noise points in the point cloud data, which has a great influence on the following point cloud filtering processing. Through the 3D space grid point, the cloud data of the inside of each cubic grid points have the property of spatial index, according to the spatial neighborhood relationship between 3D grid judgments within the grid point as noise points. Experimental results show that this algorithm can effectively filter the terrain point cloud of the discrete noise points and clusters of small noise, by using appropriate correlation coefficient of the average distance between the 3D grid sidescan which greatly reduces the error of determining noise.