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.

翻译：基于相机的感知系统在现代自动驾驶车辆中扮演着核心角色。这些基于相机的感知算法需要精确的标定，以将现实世界的距离映射到图像像素上。实际应用中，标定是一项劳动密集型工作，需要专门的数据收集和细致的调参。每当相机参数发生变化时，该过程必须重复执行——这在自动驾驶车辆中属于常见情况。因此，需要定期进行标定以确保相机精度。本文提出一种深度学习框架，用于实时学习相机的内参和外参标定。该框架是自我监督的，无需任何标签或监督即可学习标定参数。此框架无需借助物理靶标，也无需在特殊平面路面上驾驶车辆即可完成标定学习。