  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.
