Camera calibration for lane and obstacle detection

Abstract

This paper deals with camera calibration for use in guided vehicles. Camera calibration yields parameters which describe the relationship between 2D images and 3D environment. An intrinsic camera calibration is performed and the extrinsic parameters are determined using a weighted least squares technique within a robust, iterative estimator. They allow a correct 3D reconstruction from the given images. This information is indispensable for lane and obstacle detection with the goal of driving an autonomous, unsupervised vehicle. The impacts of uncertainty in the calibration parameters on lane recognition and obstacle detection are quantified. It is shown that reliable results of lane recognition and object detection can only be performed with calibration parameters of high precision.