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:连接和协作驾驶需要路侧基础设施精确校准,以保证可靠的感知系统。为了解决这一自动化需求,本文提出了一种健壮的外部标定方法,用于自动化地地理参考摄像头标定。我们的方法需要搭载了组合GNSS/RTK接收器和惯性测量单元(IMU)用于自我定位的标定车辆。为了消除标定目标的外观和当地交通情况的要求,我们提出了一种使用假设过滤的新方法。我们的方法不需要人为干预记录的基础设施和车辆信息。此外,我们没有限制其他道路用户在校准期间的道路使用权。通过对合成数据集以及真实联网路口进行评估和部署标定,我们展示了我们的方法的可行性和准确性。我们的源代码公开可用。