Automated Marker-Free Registration of Multisource Forest Point Clouds Using a Coarse-to-Global Adjustment Strategy

编号 030028101

推送时间 20210308

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 Automated Marker-Free Registration of Multisource Forest Point Clouds Using a Coarse-to-Global Adjustment Strategy

来源期刊 Forests

期 第281期

发表时间 20210226

关键词 forest point cloud;  terrestrial laser scanning;  unmanned aerial vehicle;  registration; 

摘要 Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) are two effective platforms for acquiring forest point clouds. TLS has an advantage in the acquisition of below-canopy information but does not include the data above the canopy. UAVs acquire data from the top viewpoint but are confined to the information above the canopy. To obtain complete forest point clouds and exploit the application potential of multiple platforms in large-scale forest scenarios, we propose a practical pipeline to register multisource point clouds automatically. We consider the spatial distribution differences of trees and achieve the coarse alignment of multisource point clouds without artificial markers; then, the iterative closest point method is used to improve the alignment accuracy. Finally, a graph-based adjustment is designed to remove accumulative errors and achieve the gapless registration. The experimental results indicate high efficiency and accuracy of the proposed method. The mean errors for the registration of multi-scan TLS point clouds subsampled at 0.03 m are approximately 0.01 m, and the mean errors for registration of the TLS and UAV data are less than 0.03 m in the horizontal direction and approximately 0.01 m in the vertical direction.

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