  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.
