Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined GNSS/RTK receiver and an inertial measurement unit (IMU) for self-localization. In order to remove any requirements for the target's appearance and the local traffic conditions, we propose a novel approach using hypothesis filtering. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle. Furthermore, we do not limit road access for other road users during calibration. We demonstrate the feasibility and accuracy of our approach by evaluating our approach on synthetic datasets as well as a real-world connected intersection, and deploying the calibration on real infrastructure. Our source code is publicly available.

翻译：车联网协同驾驶需要精确标定路侧基础设施以实现可靠的感知系统。为自动化解决这一需求，我们提出一种鲁棒的自动化地理参考相机外参标定方法。该方法要求标定车辆配备全球导航卫星系统/实时动态差分接收机（GNSS/RTK）与惯性测量单元（IMU）实现自定位。为消除对标定目标外观及当前交通条件的依赖，我们提出一种基于假设滤波的创新方法。本方法无需人类干预基础设施与车辆记录的标定信息，且在标定过程中不限制其他道路使用者的通行。通过在合成数据集及真实车联网交叉口场景下的评估，并在实际路侧基础设施上部署标定，我们验证了该方法的可行性与精度。相关源代码已公开。