  Hand-eye calibration is the problem of estimating the spatial transformation
between a reference frame, usually the base of a robot arm or its gripper, and
the reference frame of one or multiple cameras. Generally, this calibration is
solved as a non-linear optimization problem, what instead is rarely done is to
exploit the underlying graph structure of the problem itself. Actually, the
problem of hand-eye calibration can be seen as an instance of the Simultaneous
Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we
present a pose-graph approach to the hand-eye calibration problem that extends
a recent state-of-the-art solution in two different ways: i) by formulating the
solution to eye-on-base setups with one camera; ii) by covering multi-camera
robotic setups. The proposed approach has been validated in simulation against
standard hand-eye calibration methods. Moreover, a real application is shown.
In both scenarios, the proposed approach overcomes all alternative methods. We
release with this paper an open-source implementation of our graph-based
optimization framework for multi-camera setups.
