A Mark-Free Registration Method for Forest Airborne and Terrestrial Laser Scanning Point Clouds Fusion Based on Canopy Voxel Features

Abstract:

Accurate registration for forest airborne laser scanning （ALS） point clouds and terrestrial laser scanning （TLS） point clouds improves data integrity， which can better serve for forest ecological parameter retrieval. A marker-free registration method is proposed to fuse ALS point clouds and TLS point clouds based on canopy voxel features. The method achieves ALS-TLS point clouds registration with the following steps： bounding box orientation， density-height-based canopy point clouds filtering， canopy point clouds voxelization， tie feature extraction with sliding voxel templates， and transformation matrix calculation. The proposed method was validated by four datasets with different forest structure complexities， which shows that its average differences are 0.245m， 0.238m， 0.184m， and 0.020m respectively. The comparisons indicates that the proposed method outperforms its counterparts including iterative closest point algorithm， artificial matching， and the combination of the two.