A Stepwise Minimum Spanning Tree Matching Method for Registering Vehicle-borne and Backpack LiDAR Point Clouds

Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies for capturing detailed spatial information near ground in urban built environments. BLS has flexible mobility and usually provides point clouds in a local coordinate system. Therefore, a...

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Published in:IEEE transactions on geoscience and remote sensing Vol. 60; p. 1
Main Authors: Wu, Bin, Yang, Lei, Wu, Qiusheng, Zhao, Yi, Pan, Zhan, Xiao, Tian, Zhang, Jiarui, Wu, Jianping, Yu, Bailang
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Backpacks
BLS
Built environment
Coordinate systems
Coordinates
Data acquisition
Data mining
Datasets
Feature extraction
Graph theory
Laser applications
Laser radar
Lasers
Lidar
Matching
Methods
minimum spanning tree
Point cloud compression
registration
Scanning
Spatial data
Surveying
Three-dimensional displays
trunk centers
Urban areas
Urban environments
Vegetation
VLS
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies for capturing detailed spatial information near ground in urban built environments. BLS has flexible mobility and usually provides point clouds in a local coordinate system. Therefore, a mismatch between VLS and BLS point clouds data is quite common. Fusing VLS and BLS data in different coordinate systems could provide a comprehensive survey of urban built environments. Because of the complexity of urban road environments and the difference in data acquisition methods, traditional registration approaches based on point-level correspondences are likely to fail and sometimes involve substantial manual efforts. In this paper, we propose a novel registration approach that finds the optimal transformation between the respective point clouds based on a unique tree distribution pattern defined by tree trunk centers. The proposed method consists of three key steps, i.e., trunk center extraction, stepwise minimum spanning tree (MST) matching, and transformation estimation. Stepwise MST matching is an essential step in finding the one-to-one correspondences using a topological similarity between the two LiDAR datasets. We evaluated our method with five real-world datasets collected in the Shanghai city, China. The results showed that the proposed method performed well in all five experiment sites with an average rotation error of less than 0.06° and an average translation error of less than 0.05 m. Moreover, the reported mean position deviation in the five sites are 0.112 m, 0.144 m, 0.176 m, 0.148 m, and 0.184 m, respectively. Our proposed method has a great potential for registering multiplatform LiDAR data that could provide comprehensive and essential 3D information for numerous urban applications.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3226956