Presentation + Paper
12 April 2021 A data fusion approach to improving moving object measurement using phase-shifting profilometry
Chengpu Duan, Jiangtao Xi, Jun Tong, Yanguang Yu, Qinghua Guo
Author Affiliations +
Abstract
Phase-shifting profilometry (PSP) is one of the mainstream fringe projection techniques for object surface reconstruction. Thanks to its multi-shot nature, PSP is less sensitive to ambient light and reflectivity on the object surface. For static objects, the performance of PSP can be improved by projecting and capturing more phase-shifted fringe patterns. However, when applied to dynamic objects, PSP suffers from motion-induced errors due to the loss of correspondence. This paper proposes a new approach to improving the performance of measuring objects with a general three-dimensional (3D) movement. Firstly, instead of employing a large number of fringe patterns for PSP, we apply three-step PSP, which suffers less from motion errors, to obtain multiple coarse measurements of the object. Then the moving object is segmented, and the iterative closest point (ICP) algorithm is applied to estimate the motion parameters. Finally, the multiple measurements of the object are fused using adaptive weights. The proposed scheme alleviates the motion errors of PSP with a large number of fringe patterns and enhances the accuracy of three-step PSP. Simulations and experiments verify the feasibility of the proposed scheme.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chengpu Duan, Jiangtao Xi, Jun Tong, Yanguang Yu, and Qinghua Guo "A data fusion approach to improving moving object measurement using phase-shifting profilometry", Proc. SPIE 11732, Dimensional Optical Metrology and Inspection for Practical Applications X, 1173208 (12 April 2021); https://doi.org/10.1117/12.2591620
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KEYWORDS
Phase shifts

Data fusion

Fringe analysis

3D metrology

Motion estimation

Motion measurement

Reflectivity

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