Simultaneous Optimization of Hand-Eye and Robot-World Parameters Exploiting Accuracy Discrepancies in Robotic and Vision Sensor

Accurate hand-eye and robot-world calibration remains crucial for vision-guided robotic applications. Existing methods predominantly focus on deriving closed-form or nonlinear optimization solutions for hand-eye equationAX=XBor hand-eye-robot-world calibration equationAX=YBunder various rotation parameterizations, while the critical role of input data refinement on calibration accuracy has been largely overlooked. This paper addresses this gap by proposing a pose graph optimization model that strategically enhances the precision of key pose chains in robotic vision systems, particularly under significant discrepancies between robotic positioning and visual measurement accuracies. Building upon the optimized pose chains associated with the hand-eye and robot-world parameters, a pair of dual equations forXandYis then constructed and solved via our proposed Kronecker product-based least squares algorithm, allowing simultaneous and accurate estimation on rotation matrices and translation components ofXandY. Simulation and real-world experimental results have validated the superiority of the proposed pose graph optimization model, under significant robot-vision precision disparities. Moreover, even when the error magnitudes between the robot and visual measurement system are comparable, the proposed method achieves superior accuracy for both the hand-eye and robot-world parameters compared to nine state-of-the-art approaches.