Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1433-1438, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1433-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1433-1438, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1433-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A VOXEL-BASED FILTERING ALGORITHM FOR MOBILE LIDAR DATA

H. Qin1, G. Guan1, Y. Yu2, and L. Zhong3 H. Qin et al.
  • 1College of Remote Sensing and Geometics Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 2Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
  • 3Changjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 410010, China

Keywords: Mobile LiDAR, filtering, voxelization, upward growing, curvature

Abstract. This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.