A kernel function based on Gaussian surface construction for fast and high-precision extraction of centers of line-structure light

Abstract Line-structure light three-dimensional (3D) measurement is widely applied in the field of non-contact 3D metrology. The curve fitting method is a frequently used algorithm for extracting the center of laser stripes. With respect to the calculation of the sampling direction in the curve fitting method, this paper constructs a kernel function for solving the normal direction of the pixel-level center of the laser stripe, based on the characteristic that the line-structure light follows a Gaussian distribution in the normal direction. By calculating the sampling points within a certain range of the pixel-level center using the kernel function, the first-order and second-order derivatives at the center could be directly obtained, thereby obtaining the normal direction at the center. To enhance the robustness of the sampling data in the normal direction and the real-time processing capability, this paper proposed the calculation of adaptive sampling width and prediction based on the region of interest of the Kalman filtered image respectively. The experimental results demonstrated that the proposed method attains a spherical surface fitting accuracy of 0.0251 mm for the standard target ball. The pre-extraction speed of the stripes is increased by 228% compared to global detection, and the sub-pixel center extraction speed reaches 0.91 milliseconds per frame.