Yamakawa Laboratory, The University of Tokyo

東京大学　山川研究室

This paper proposes a simple yet effective markerless hand-eye calibration method that achieves low cost, high accuracy, and strong generalization across different types of robots. The method utilizes a circular flange, a standardized structure in industrial robots, for calibration via the perspective-n-point (PnP) algorithm, achieving superior performance with a simpler pipeline. The entire system is built using mature, off-the-shelf components, avoiding complex architectures. By combining a lightweight object detection network (e.g., Faster R-CNN) with classical geometric techniques, we construct a flange detector that is both accurate and robust. The training process requires no manual annotations, and the resulting model generalizes well across various robot platforms. Experiments demonstrate that our method achieves higher calibration accuracy than more complex existing approaches. Notably, the method maintains consistent precision even when applied to previously unseen robots.

All developments are available at: https://github.com/Ruoyu-Jia/Markerless_hand_eye.

• Ruoyu Jia, Ruomeng Fan, Qitong Guo, Xiaohang Shi, Masahiro Hirano and Yuji Yamakawa: Markerless Hand-Eye Calibration by Flange Ellipse Detection, The 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)