Efficient and robust extrinsic camera calibration procedure for Lane Departure Warning

Intelligent driver assistance systems, such as lane departure warning, extract 3D information of the road geometry from a camera. Therefore, the transformation between the image and the ground plane has to be determined with a very high accuracy. Conventional calibration methods are usually a compromise between the accuracy and a preferably small effort for the calibration set-up. In this paper, we present an efficient and robust method for an accurate estimation of the extrinsic parameters based on minimizing an error function. The idea is to avoid the difficult and time-consuming measurement of marker positions in the 3D world coordinate system which is fixed with respect to the vehicle. A pattern of circles is placed on the ground plane in front of the car. For our approach, it is only necessary to measure the relative distances between the centers of the circles to each other. A nonlinear-optimization algorithm minimizes the squared difference between the distances of the backprojected circles segmented in the images on the ground plane and of the measurement in the real world.