Automated Static Camera Calibration with Intelligent Vehicles
This addresses the need for precise, automated calibration of roadside infrastructure in cooperative driving, though it is incremental as it builds on existing sensor-based calibration methods.
The paper tackles the problem of automated extrinsic calibration for roadside cameras in connected driving systems, presenting a method that uses a calibration vehicle with GNSS/RTK and IMU for self-localization and hypothesis filtering to remove requirements for target appearance and traffic conditions, achieving feasibility and accuracy demonstrated on synthetic datasets and a real-world intersection.
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.