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Open AccessArticle

Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud

by Liang Cheng 1,2,3,4, Yang Wu 4, Lihua Tong 4, Yanming Chen 1,2,4,* and Manchun Li 1,2,4,5,*
1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China
2
Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210093, China
3
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, Nanjing 210093, China
4
Department of Geographic Information Science, Nanjing University, Nanjing 210093, China
5
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä, Norman Kerle and Prasad S. Thenkabail
Remote Sens. 2015, 7(10), 13921-13944; https://doi.org/10.3390/rs71013921
Received: 13 July 2015 / Revised: 6 October 2015 / Accepted: 15 October 2015 / Published: 23 October 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy. View Full-Text
Keywords: airborne LiDAR; vehicle LiDAR; 3D road network; 3D building contour; point cloud registration airborne LiDAR; vehicle LiDAR; 3D road network; 3D building contour; point cloud registration
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MDPI and ACS Style

Cheng, L.; Wu, Y.; Tong, L.; Chen, Y.; Li, M. Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud. Remote Sens. 2015, 7, 13921-13944.

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