Share Email Print
cover

Proceedings Paper

Registration of multi-view point clouds based on nonlinear correction
Author(s): Yunlan Guan; Xiaojun Cheng; Guigang Shi; Wei Li
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Terrestrial laser scanning is a new technology uprising in 1990's. Owing to its ability to obtain a large number of 3D coordinates of points, called point clouds, in real time, it has been attracted attentions of surveying field. To build threedimensional models, multiple scans from different viewpoints are required due to occluded surfaces and limited field of view of the scanner. These multi-view point clouds then must be transformed into a common reference frame in order to describe complete object being researched. This process is called registration of point clouds. Pairwise registration often is a common method for registration of multi-views point clouds. Owing to characteristics of error accumulation and propagation, using pairwise registration method will result in severely distortion of researched object. In order to overcome this shortcoming, we present a new registration method. Firstly, we use pairwise registration method to calculate transformation parameters of adjacent two scans, and by selecting coordinate system of first scan as a uniform coordinate system, we connect all point clouds into a loosely network. Secondly, according to condition that common points between first scan and last scan must have the same coordinates in uniform system, we use nonlinear correction model to compute the distortion parameters of loosely network and lastly we correct distortion of each single point clouds and determine the best position of each point. Experiment is carried out and the results show that the registration error has reduced from 1.7cm to 5mm after correction, which demonstrates correctness of the method.

Paper Details

Date Published: 13 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74920C (13 October 2009); doi: 10.1117/12.838211
Show Author Affiliations
Yunlan Guan, East China Institute of Technology (China)
Xiaojun Cheng, Tongji Univ. (China)
Guigang Shi, Tongji Univ. (China)
Wei Li, East China Institute of Technology (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

© SPIE. Terms of Use
Back to Top