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.

翻译：传感器标定对于自动驾驶至关重要，它为精确定位与一致性数据融合提供了基础。为实现高精度GNSS传感器的应用，本研究聚焦于天线杆臂标定问题。我们提出一种基于运动测量的全局最优多天线杆臂标定方法。为此，我们推导出一种优化方法，该方法进一步支持先验知识的融合。通过利用拉格朗日对偶问题及原始恢复策略，我们获得了全局最优解。通常，基于运动的自动驾驶车辆标定因车辆主要进行平面运动而难以实现。因此，我们首先描述了获得唯一解所需的运动条件，随后提出一种平面运动扩展方法以克服此限制，使得基于自动驾驶车辆受限运动的标定成为可能。最后，我们展示并讨论了全面评估的结果。通过使用仿真数据与增强现实数据，我们实现了精确的标定结果与满足在线部署需求的快速运行时间。