Authors:
Kai Cordes
and
Hellward Broszio
Affiliation:
VISCODA GmbH, Hannover and Germany
Keyword(s):
Multi Camera Calibration, Lane Merge, Multi View, Vehicle Localization.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Device Calibration, Characterization and Modeling
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
For the trajectory planning in autonomous driving, the accurate localization of the vehicles is required. Accurate localizations of the ego-vehicle will be provided by the next generation of connected cars using 5G. Until all cars participate in the network, un-connected cars have to be considered as well. These cars are localized via static cameras positioned next to the road. To achieve high accuracy in the vehicle localization, the highly accurate calibration of the cameras is required. Accurately measured landmarks as well as a priori knowledge about the camera configuration are used to develop the proposed constrained multi camera calibration technique. The reprojection error for all cameras is minimized using a differential evolution (DE) optimization strategy. Evaluations on data recorded on a test track show that the proposed calibration technique provides adequate calibration accuracy while the accuracies of reference implementations are insufficient.