Sensor calibration is crucial for autonomous driving, providing the basis for accurate localization and consistent data fusion. Enabling the use of high-accuracy GNSS sensors, this work focuses on the antenna lever arm calibration. We propose a globally optimal multi-antenna lever arm calibration approach based on motion measurements. For this, we derive an optimization method that further allows the integration of a-priori knowledge. Globally optimal solutions are obtained by leveraging the Lagrangian dual problem and a primal recovery strategy. Generally, motion-based calibration for autonomous vehicles is known to be difficult due to cars' predominantly planar motion. Therefore, we first describe the motion requirements for a unique solution and then propose a planar motion extension to overcome this issue and enable a calibration based on the restricted motion of autonomous vehicles. Last we present and discuss the results of our thorough evaluation. Using simulated and augmented real-world data, we achieve accurate calibration results and fast run times that allow online deployment.
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