Automated Automotive Radar Calibration With Intelligent Vehicles
This work addresses the need for precise radar calibration in cooperative driving scenarios, representing an incremental improvement over manual calibration methods.
The paper tackles the problem of time-consuming and labor-intensive extrinsic calibration of automotive radar sensors for environment perception by presenting an automated, geo-referenced calibration method based on a novel hypothesis filtering scheme, which correctly calibrates infrastructure sensors using data from a real testing site.
While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.