Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications
This addresses the problem of maintaining accurate camera calibration for autonomous driving and parking systems, reducing manual effort and enabling continuous operation, though it appears incremental as it builds on existing self-supervised and deep learning approaches.
The paper tackles the laborious and frequent need for camera calibration in autonomous vehicles by proposing a self-supervised deep learning framework that learns intrinsic and extrinsic calibration parameters in real time without requiring labels, physical targets, or special driving surfaces.
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.