Markerless Hand-Eye Calibration by Flange Ellipse Detection

Abstract

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. Code and pre-trained models will be made available.