Abstract: The point cloud filtering process is a very important part of LiDAR data processing, that is, to separate ground points and non-ground points in point cloud   data so as to lay the foundation for subsequent data processing.  Based on the traditional progressive mathematical morphology filtering and cloth simulation filtering methods， this paper considers that the effect of progressive morphological filtering on ground point separation is acceptable, that is, it can basically retain all ground points. However, due to the weak adaptability of terrain, the height difference threshold is also unstable with the change of terrain slope, some non-ground points are easy to be regarded as ground points, and cloth simulation filtering has the advantage of high efficiency of algorithm operation, and the filtering effect of cloth simulation filtering in flat terrain is better than that in areas with large terrain undulations. Based on the results of progressive morphological filtering, the coarse DEM raster data of the target area is established, and then the elevation value of the target point cloud is normalized to eliminate the influence of terrain changes on the cloth simulation filtering. Finally, the experimental results of using three sets of standard data samples on the official website of ISPRS show that the type I error is reduced compared to the result of progressive morphological filtering, the type II error is reduced compared to the result of cloth simulation filtering, and the total error is also reduced, so a better filtering effect is achieved.

Key words: point cloud data, cloth simulation filtering, accuracy, mathematical morphology filtering, DEM, normalized