2025
Generative Adversarial Networks for Solving Hand-Eye Calibration Without Data Correspondence
RA-L 2025
In this study, we rediscovered the framework of generative adversarial networks (GANs) as a solver for calibration problems without data correspondence. When data correspondence is not present or loosely established, the calibration problem becomes a parameter estimation problem that aligns the two