Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

翻译：精确标定是视觉引导机器人系统实现高精度操作的基础。多眼（相机）与多手（机器人）系统对标定误差尤为敏感，例如微装配系统。现有方法大多聚焦于整个系统中单个单元的标定，如手眼之间的位姿或两机器人之间的位姿。这些方法可用于确定各单元间的相对位姿，但在大规模系统中，串行化增量标定策略无法避免误差累积问题。本文不局限于单个单元，而是将多眼与多手系统标定问题建模为图，并提出一种基于最小生成树与图优化的方法。该方法能够根据系统配置自动规划串行化最优标定策略，首先获得粗标定结果；随后以这些初始值为基础，引入闭环约束进行全局优化。仿真实验验证了所提算法在不同噪声及不同手眼配置下的性能。此外，进一步在真实机器人系统上开展了实验以验证该方法。