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Sensors 2018, 18(2), 544; doi:10.3390/s18020544

A Maximum Feasible Subsystem for Globally Optimal 3D Point Cloud Registration

1
Department of Media Technology, Sogang University, Seoul 04107, Korea
2
Yonsei Institute of Convergence Technology, Yonsei University, Incheon 21993, Korea
*
Author to whom correspondence should be addressed.
Received: 11 January 2018 / Revised: 5 February 2018 / Accepted: 7 February 2018 / Published: 10 February 2018
(This article belongs to the Section Remote Sensors)
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Abstract

In this paper, a globally optimal algorithm based on a maximum feasible subsystem framework is proposed for robust pairwise registration of point cloud data. Registration is formulated as a branch-and-bound problem with mixed-integer linear programming. Among the putative matches of three-dimensional (3D) features between two sets of range data, the proposed algorithm finds the maximum number of geometrically correct correspondences in the presence of incorrect matches, and it estimates the transformation parameters in a globally optimal manner. The optimization requires no initialization of transformation parameters. Experimental results demonstrated that the presented algorithm was more accurate and reliable than state-of-the-art registration methods and showed robustness against severe outliers/mismatches. This global optimization technique was highly effective, even when the geometric overlap between the datasets was very small. View Full-Text
Keywords: pairwise three-dimensional (3D) registration; 3D measurement; point cloud alignment; outlier removal; maximum feasible subsystem pairwise three-dimensional (3D) registration; 3D measurement; point cloud alignment; outlier removal; maximum feasible subsystem
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Yu, C.; Ju, D.Y. A Maximum Feasible Subsystem for Globally Optimal 3D Point Cloud Registration. Sensors 2018, 18, 544.

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