Structured light reconstruction accuracy compensation method based on discrete error analysis

Various sources of measurement errors can compromise the accuracy of three-dimensional (3D) surface topography measurement systems in complicated situations with high ambient light noise. The impact of ambient light on measurement accuracy stands out among the others. To improve the accuracy of surface structure light reconstruction, this academic work suggests a novel compensation technique that focuses on the multiple error model fitting scheme. Discrete error analysis (DEA) is incorporated into the method, and a Double Perceived Phase Shifting (DPPS) algorithm is introduced. To create a binocular structured light reconstruction system utilizing the triangulation approach, the research first analyzes the 3D reconstruction algorithm of structured light based on spatial phase unfolding. Then, the analysis concentrates on the measuring system's grating acquisition error while taking into account the effects of various noise sources on the streak acquisition in the binocular reconstruction system. The sources of the error are categorized, and a Gaussian mixed stochastic model and a periodic error model are used to build the measurement system's overall error model. The optimal fringe is then obtained, and the mapping relationship between the projection and camera acquisition is improved for greater spatial fringe, by applying the DEA approach to assess the impact of the inaccuracy on the phase error of the structured light. Ultimately, a comparative study is carried out to evaluate the effectiveness of the suggested method. The collected findings show that the algorithm is stable and has high-precision measuring capabilities. The approach, which doesn't require extra phase shifting steps, notably shows notable improvements over traditional techniques like the Dual-Frequency Pattern Scheme (DFPS) and Third Harmonic Injection Algorithm (THIA). The DEA+DPPS achieved an accuracy improvement of 23.92% on average.