Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications

TL;DR

This paper introduces a self-supervised deep learning framework that enables real-time calibration of vehicle cameras without physical targets, reducing manual effort and enabling frequent recalibration in autonomous driving and parking.

Contribution

It presents a novel self-supervised approach for online intrinsic and extrinsic camera calibration that operates without physical calibration targets or specialized driving conditions.

Findings

Achieves real-time calibration accuracy suitable for autonomous driving.

Eliminates the need for physical calibration targets or special surfaces.

Reduces manual calibration effort and frequency.

Abstract

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.