Natural forest ALS-TLS point cloud data registration without control points

doi:10.1007/s11676-022-01499-w

Abstract

Abstract:

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) has attracted attention due to their forest parameter investigation and research applications. ALS is limited to obtaining fine structure information below the forest canopy due to the occlusion of trees in natural forests. In contrast, TLS is unable to gather fine structure information about the upper canopy. To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform, this study proposes data registration without control points. The ALS and TLS original data were cropped according to sample plot size, and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin. The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data. The initial registered point cloud data was finely and optimally registered via the iterative closest point (ICP) algorithm. The results show that the proposed method achieved high-precision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch. and Picea asperata Mast. which included different species and environments. An average registration accuracy of 0.06 m and 0.09 m were obtained for P. yunnanensis and P. asperata, respectively.

Key words: Airborne laser scanning (ALS), Terrestrial laser scanning (TLS), Registration, Natural forest, Iterative closest point (ICP) algorithm

Jianpeng Zhang, Jinliang Wang, Feng Cheng, Weifeng Ma, Qianwei Liu, Guangjie Liu. Natural forest ALS-TLS point cloud data registration without control points[J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(3): 809-820.

Jianpeng Zhang, Jinliang Wang, Feng Cheng, Weifeng Ma, Qianwei Liu, Guangjie Liu. [J]. 林业研究(英文版), 2023, 34(3): 809-820.

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