Iterative automatic global registration algorithm for multi-view point cloud of underground tunnel space

被引:8
作者
Guo, Ming [1 ,2 ,3 ,4 ]
Yan, Bingnan [1 ]
Wang, Guoli [1 ,2 ,3 ,4 ]
Nie, Pingjun [5 ]
Pan, Deng [6 ]
Guo, Kecai [7 ]
Liu, Yunming [8 ]
机构
[1] Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing, Peoples R China
[2] Minist Educ, Engn Res Ctr Representat Bldg & Architectural Her, Beijing, Peoples R China
[3] Natl Adm Surveying Mapping & Geoinformat, Key Lab Modern Urban Surveying & Mapping, Beijing, Peoples R China
[4] Beijing Key Lab Architectural Heritage Fine Recon, Beijing, Peoples R China
[5] Beijing Univ Civil Engn & Architecture, Sch Humanity & Law, Beijing, Peoples R China
[6] Beijing Univ Civil Engn & Architecture, Sch Civil & Transportat Engn, Beijing, Peoples R China
[7] Beijing Shenxindacheng Technol Co Ltd, Beijing, Peoples R China
[8] Beijing Urban Construct Explorat & Surveying Desi, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
Point cloud; tunnel space; iterative global registration; Rodrigues matrix; accuracy analysis;
D O I
10.1177/00202940211003935
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the narrow and long tunnel structure, few internal features, and a large amount of point cloud data, the existing registration algorithms and commercial software registration results are not ideal, an iterative global registration algorithm is proposed for massive underground tunnel point cloud registration, which is composed of local initial pose acquisition and global adjustment. Firstly, the feature point coordinates in the point cloud are extracted, and then the station-by-station registration is performed according to the Rodrigues matrix. Finally, the registration result is considered as the initial value of the parameter, and the global adjustment of all observations is carried out. The observation values are weighted by the selection weight iteration method and the weights are constantly modified in the iteration process until the threshold conditions are met and the iteration stops. In this paper, the experimental data, made up of 85 stations of point cloud data, are from the Xiamen subway tunnel, which is about 1300 m long. When the accumulated error of station-to-station registration is too large, several stations are regarded as partial wholes, and the optimal registration is achieved through multiple global adjustments, and the registration accuracy is within 5 mm. Experimental results confirm the feasibility and effectiveness of the algorithm, which provides a new method for point cloud registration of underground space tunnel.
引用
收藏
页码:385 / 395
页数:11
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