Self-Supervised Online Camera Calibration for Automated Driving and
  Parking Applications
Ciarán Hogan Ganesh Sistu Ciarán Eising
Camera-based perception systems play a central role in modern autonomous
vehicles. These camera based perception algorithms require an accurate
calibration to map the real world distances to image pixels. In practice,
calibration is a laborious procedure requiring specialised data collection and
careful tuning. This process must be repeated whenever the parameters of the
camera change, which can be a frequent occurrence in autonomous vehicles. Hence
there is a need to calibrate at regular intervals to ensure the camera is
accurate. Proposed is a deep learning framework to learn intrinsic and
extrinsic calibration of the camera in real time. The framework is
self-supervised and doesn't require any labelling or supervision to learn the
calibration parameters. The framework learns calibration without the need for
any physical targets or to drive the car on special planar surfaces.