Error Analysis and Accuracy Optimization for the Hand-eye Calibration of Line Structured Light

Abstract

Abstract The rapid development of machine vision technology in recent years has resulted in applications in engineering scenarios such as weld tracking, visual measurement, and precision gripping. Focusing on the fundamental steps of applying machine vision technology, the “hand-eye calibration” process is analyzed based on scenarios with a line-structured light camera and calibration ball. We found out that noise and camera pose are the main sources of calibration error. To address these issues, an improved K-medoids noise pre-processing algorithm and a camera shooting attitude control scheme are proposed and applied. The experimental results showed that when the radius of the tangent circle/radius of the calibration sphere approximates 0.618, the root means a square error of the repeatability accuracy of hand-eye calibration could be controlled within 0.23 mm, which fully meets the application requirement. Considering the low cost of line-structured light cameras, the findings of this study contribute to the large-scale diffusion of machine vision technology in engineering practices.