In industrial scenarios, effective human-robot collaboration relies on multi-camera systems to robustly monitor human operators despite the occlusions that typically show up in a robotic workcell. In this scenario, precise localization of the person in the robot coordinate system is essential, making the hand-eye calibration of the camera network critical. This process presents significant challenges when high calibration accuracy should be achieved in short time to minimize production downtime, and when dealing with extensive camera networks used for monitoring wide areas, such as industrial robotic workcells. Our paper introduces an innovative and robust multi-camera hand-eye calibration method, designed to optimize each camera's pose relative to both the robot's base and to each other camera. This optimization integrates two types of key constraints: i) a single board-to-end-effector transformation, and ii) the relative camera-to-camera transformations. We demonstrate the superior performance of our method through comprehensive experiments employing the METRIC dataset and real-world data collected on industrial scenarios, showing notable advancements over state-of-the-art techniques even using less than 10 images. Additionally, we release an open-source version of our multi-camera hand-eye calibration algorithm at https://github.com/davidea97/Multi-Camera-Hand-Eye-Calibration.git.

翻译：在工业场景中，有效的人机协作依赖于多相机系统，以在机器人工作单元内普遍存在遮挡的情况下，鲁棒地监控操作人员。在此场景下，人员在机器人坐标系中的精确定位至关重要，这使得相机网络的手眼标定成为关键环节。当需要在短时间内实现高标定精度以最小化生产停机时间，以及处理用于监控广阔区域（如工业机器人工作单元）的大规模相机网络时，该过程面临重大挑战。本文提出了一种创新且鲁棒的多相机手眼标定方法，旨在优化每个相机相对于机器人基座以及相对于其他每个相机的位姿。该优化过程整合了两种关键约束：i) 单一的标定板到末端执行器的变换，以及 ii) 相机到相机之间的相对变换。我们通过使用METRIC数据集和在工业场景中采集的真实数据进行综合实验，证明了我们方法的优越性能，即使使用少于10张图像，也显示出相较于现有先进技术的显著进步。此外，我们在 https://github.com/davidea97/Multi-Camera-Hand-Eye-Calibration.git 发布了我们多相机手眼标定算法的开源版本。