A Generic Vehicle-to-Sensor Calibration Framework

Sumin Hu, Youngmin Yoo, Jeeseong Kim, Changsoo Lim, Doohyun Cho, Bongnam Kang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5980-5989

In autonomous driving systems online vehicle-to-sensor (v2s) calibration is a critical component for ensuring safe perception-based control. Since sensor pose may shift during the life-time of a vehicle online calibration is essential to maintain safe driving conditions. To this end this paper introduces Epipoles as a 3D Directional Compass (E3DC) a sensor-agnostic v2s online calibration method. Leveraging the nonholonomic nature of vehicles a hand-eye constraint between the vehicle and the sensor naturally emerges. Consequently we require only the sensor's data to determine the v2s extrinsic rotation. More specifically since we only require sensor odometry estimates to perform v2s calibration E3DC can leverage off-the-shelf odometry estimation pipelines. This offers vast flexibility and wide applicability as the odometry estimation pipeline can be tailored to the specific sensor type and driving environment. We demonstrate that our method is robust and achieves state-of-the-art performance on both the KITTI dataset and a new dataset which will be made publicly available. To the best of our knowledge this is the first v2s calibration dataset for autonomous driving scenarios.