Research on the principle and improvement method of hand-eye calibration based on limited-memory with bound constraints

Abstract

Technology of hand-eye calibration based on monocular vision is used widely in the field of industrial robots. For the eye-to-hand calibration, an improved nine-point method is proposed, in which a suction cup tool is used instead of a calibration plate to avoid the experimental error caused by the displacement of the calibration plate and end of robotic arm during the calibration process, so that the calibration can be completed quickly while the calibration accuracy is guaranteed, and the error can be controlled within ±0.04 mm. For the eye-in-hand calibration mode, an improved non-contact calibration method is proposed, which obtains the size and range of the initial values through the installation mode of the camera and the robotic arm, establishes the conversion equation from the pixel coordinates to the base coordinates of the robotic arm through these initial values, and then performs the hand-eye matrix solving through the L-BFGS-B (Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bound constraints) algorithm. After many experimental evidence, this method can control the calibration error within ±0.1 mm, and the calibration consumes time within 5 min, which effectively improves the calibration efficiency and calibration accuracy.