Automated Automotive Radar Calibration With Intelligent Vehicles
Alexander Tsaregorodtsev Michael Buchholz Vasileios Belagiannis
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.