We propose a certifiably globally optimal approach for solving the hand-eye robot-world problem supporting multiple sensors and targets at once. Further, we leverage this formulation for estimating a geo-referenced calibration of infrastructure sensors. Since vehicle motion recorded by infrastructure sensors is mostly planar, obtaining a unique solution for the respective hand-eye robot-world problem is unfeasible without incorporating additional knowledge. Hence, we extend our proposed method to include a-priori knowledge, i.e., the translation norm of calibration targets, to yield a unique solution. Our approach achieves state-of-the-art results on simulated and real-world data. Especially on real-world intersection data, our approach utilizing the translation norm is the only method providing accurate results.

翻译：我们提出一种可证明全局最优的方法，用于求解同时支持多个传感器和多个目标的手眼机器人-世界问题。进一步，我们利用该公式对基础设施传感器进行地理参考标定。由于基础设施传感器记录的车载运动大多是平面运动，若不引入额外知识，无法获得相应手眼机器人-世界问题的唯一解。因此，我们扩展所提方法，加入先验知识（即标定目标的平移范数），以得到唯一解。我们的方法在仿真数据和真实世界数据上均达到了最先进水平。尤其是在真实路口数据上，利用平移范数的本方法是唯一能提供准确结果的方法。