Research Papers

Trimmed strategy for affine registration of point sets

[+] Author Affiliations
Yaxin Peng

Shanghai University, Department of Mathematics, Shanghai 200444, China

Shihui Ying

Shanghai University, Department of Mathematics, Shanghai 200444, China

Jing Qin

University of California, Department of Mathematics, Los Angeles 90095, California

Tieyong Zeng

Hong Kong Baptist University, Department of Mathematics, Hong Kong, China

J. Appl. Remote Sens. 7(1), 073468 (Dec 05, 2013). doi:10.1117/1.JRS.7.073468
History: Received September 26, 2013; Revised October 31, 2013; Accepted November 4, 2013
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Abstract.  We propose a trimmed strategy for affine registration of point sets using the Lie group parameterization. All affine transformations form an affine Lie group, thus finding an optimal transformation in registration is reduced to finding an optimal element in the affine group. Given two point sets (with outliers) and an initial element in the transformation group, we seek the optimal group element iteratively by minimizing an energy functional. This is conducted by sequentially finding the closest correspondence of two point sets, estimating the overlap rate of two sets, and finding the optimal affine transformation via the exponential map of the affine group. This method improves the trimmed iterative closest point algorithm (TrICP) in two aspects: (1) We use the Lie group parameterization to implement TrICP. (2) We also extend TrICP to the case of affine transformations. The performance of the proposed algorithm is demonstrated by using the LiDAR data acquired in the Mount St. Helens area. Both visual inspections and evaluation index (root mean trimmed squared distance) indicate that our algorithm performs consistently better than TrICP and other related algorithms, especially in the presence of outliers and missing points.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Yaxin Peng ; Shihui Ying ; Jing Qin and Tieyong Zeng
"Trimmed strategy for affine registration of point sets", J. Appl. Remote Sens. 7(1), 073468 (Dec 05, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073468


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