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.

翻译：手眼标定是估计参考坐标系（通常是机械臂基座或其夹爪的参考系）与一个或多个相机参考坐标系之间空间变换的问题。通常，该标定被求解为一个非线性优化问题，但鲜有研究利用问题本身的底层图结构。实际上，手眼标定问题可视为同时定位与建图（SLAM）问题的一个实例。受此启发，本文提出了一种面向手眼标定问题的位姿图方法，该方法在两个方面对现有最新解决方案进行了扩展：i) 针对单相机眼在基座（eye-on-base）配置提出求解公式；ii) 覆盖多相机机器人系统。所提方法在仿真环境中与标准手眼标定方法进行了对比验证，并展示了实际应用案例。在两种场景下，所提方法均优于所有替代方案。本文还开源了针对多相机系统的基于图优化框架的实现代码。