CAMERA SELF-CALIBRATION: DEEP LEARNING FROM DRIVING SCENES - Rachman, Arya; Seiler, Jürgen; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2022)
Prior to driving, cameras embedded in an autonomous driving system need to be calibrated intrinsically. Calibration is crucial to ensure that safety-related perception functions can reliably perceive the environment. Vehicle cameras are also exposed to mechanical perturbations requiring periodic re-calibration with regular uses. The current widely-accepted calibration approaches are based on robust but potentially demanding target-based methods. Such methods require a car to be taken offline and rely on static infrastructure and operators. Targetless online calibration approaches exist but remain largely unadopted due to the accuracy gaps compared to the classical methods. We propose a deep-learning-based self-calibration strategy for the vehicular camera that learns from driving scenes-they make an inherently large-scale dataset-and is validated back-to-back against checkerboard reprojection error. Our approach results in a 2.5% decrease in subpixel reprojection error compared to the existing deep-learning-based approaches. We also demonstrate its practical application in the automotive domain.
CRIS-Team Scopus Importer:2024-12-16
Keywords: autonomous vehicle; camera; deep learning; self-calibration