High-precision autocollimation method based on a multiscale convolution neural network for angle measurement

Abstract

A high-precision autocollimation method based on multiscale convolution neural network (MSCNN) for angle measurement is proposed. MSCNN is integrated with the traditional measurement model. Using the multiscale representation learning ability of MSCNN, the relationship between spot shape (large-scale feature), gray distribution (small-scale feature), and the influence of aberration and assembly error in the collimating optical path is extracted. The constructed accurate nonlinear measurement model directly improves the uncertainty of angle measurement. Experiments demonstrate that the extended uncertainty reaches 0.29 arcsec (k = 2), approximately 7 times higher than that with the traditional measurement principle, and solves the nonlinear error caused by aberration and assembly error in the autocollimation system. Additionally, this method has a good universality and can be applied to other autocollimation systems.