A data fusion approach to improving moving object measurement using phase-shifting profilometry

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Proceedings of SPIE - The International Society for Optical Engineering


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.

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