Automatic Registration Based on Covariance Matrix Eigenvector Direction of Feature Point

Abstract:

A number of range images taken from different views to be merged to construct an entire 3D model are a common problem which is called registration in RE. The enormous computational cost is required, because this process is usually repeated on searching optimal rigid body coordinate transformation parameters and evaluated in a statistical error distance between two data. This paper proposes a new registration method based on the covariance matrix eigenvector direction of feature point. First, a very fast feature extraction method is presented. Then, covariance matrix eigenvector direction of feature points and those neighborhood points are calculated. Finally, an initial estimate for relative rigid-body transform can be realized, matching these eigenvector directions using an approach of Hough transform. Experimental results of 3D images taken by laser scanner are carried out to compare the convergence and registration error. The proposed registration approach can realize automatic registration without any assumptions about their initial positions and overcome the problems of traditional ICP in low overlapping and bad initial estimate.

• Periodical: Advanced Materials Research (Volumes 538-541)
• Pages: 2842-2845
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.538-541.2842
• Online since: June 2012
• Authors: Li Hua Fan, Qi Chen, Bao Ling Xie, Bo Liu
• Keywords: Automatic Registration, Feature Extraction, Hough Transform (HT), Iterative Closest Point

References

[1] Natassha, G., Leslie I., Szymon R.: Geometrically Stable Sampling for the ICP Algorithm. Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM'03), pp.260-267(2003).

DOI: 10.1109/im.2003.1240258

[2] Besl, P. J. and McKay, N.D. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 -2, pp.239-256(1992).

[3] Dmitry, C., Dmitry, S., Pavel, K.: Image and Vision Computing, 23, pp.299-309 (2005).

[4] Luciano Silva, Olga R.P. B., Kim, L. B. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27-5, pp.762-776(2005).

[5] G. Guy and G. Medioni. IEEE Transactions on Pattern Analysis and Machine Intelligence,19-11, pp.1265-1277(1997).

DOI: 10.1109/34.632985

[6] S. Gumhold, X.L. Wang, R. Macleod: Feature Extration from Point Clouds. Proceeding of the 10 International MesP.G. Clem, M. Rodriguez, J.A. Voigt and C.S. Ashley, U.S. Patent 6,231,666. (2001) .