Hand Eye Calibration

Hand-eye calibration aims to determine the precise spatial relationship between a robot's end-effector and a mounted sensor, enabling accurate sensor data integration into the robot's coordinate system for tasks like object manipulation and navigation. Current research emphasizes markerless methods, leveraging deep learning architectures like Generative Adversarial Networks (GANs) and 3D foundation models to automate calibration and reduce reliance on manual setup. These advancements improve the accuracy, speed, and robustness of hand-eye calibration, impacting various fields including robotics, manufacturing, and minimally invasive surgery by facilitating more precise and efficient automation.