Abstract

Abstract. Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

Highlights

  • INTRODUCTIONmobile laser scanning (MLS) technology can accurately and quickly acquire three-dimensional LiDAR (light detection and ranging) point cloud of earth surface

  • mobile laser scanning (MLS) technology can accurately and quickly acquire three-dimensional LiDAR point cloud of earth surface

  • We explored the performance of the Cloth simulation filtering (CSF) algorithm for mobile LiDAR point cloud

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Summary

INTRODUCTION

MLS technology can accurately and quickly acquire three-dimensional LiDAR (light detection and ranging) point cloud of earth surface. The aforementioned ground filtering algorithms has proven to be successful for airborne LiDAR point cloud. These algorithms commonly have the following problems: (1) parameters setting are complicated; (2) filtering results are usually unreliable in complex areas; (3) most of them are not open source. Types Point density Distribution feature Spatial feature Complexity feature Building feature To simulate this physical process, CSF algorithm utilizes a cloth simulation technique to separate point clouds into ground and non-ground points (Zhang et al, 2016).

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