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Sensors 2017, 17(1), 72; doi:10.3390/s17010072

High-Precision Registration of Point Clouds Based on Sphere Feature Constraints

1
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3
School of Engineering, University of Warwick, Coventry CV4 7AL, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 24 November 2016 / Revised: 22 December 2016 / Accepted: 27 December 2016 / Published: 30 December 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [5779 KB, uploaded 30 December 2016]   |  

Abstract

Point cloud registration is a key process in multi-view 3D measurements. Its precision affects the measurement precision directly. However, in the case of the point clouds with non-overlapping areas or curvature invariant surface, it is difficult to achieve a high precision. A high precision registration method based on sphere feature constraint is presented to overcome the difficulty in the paper. Some known sphere features with constraints are used to construct virtual overlapping areas. The virtual overlapping areas provide more accurate corresponding point pairs and reduce the influence of noise. Then the transformation parameters between the registered point clouds are solved by an optimization method with weight function. In that case, the impact of large noise in point clouds can be reduced and a high precision registration is achieved. Simulation and experiments validate the proposed method. View Full-Text
Keywords: 3D measurement; point cloud; registration; virtual overlapping areas; feature constraint 3D measurement; point cloud; registration; virtual overlapping areas; feature constraint
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, J.; Wang, Z.; Gao, J.; Huang, Y.; Towers, D.P. High-Precision Registration of Point Clouds Based on Sphere Feature Constraints. Sensors 2017, 17, 72.

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