No abstract

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 timeconsuming 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.

This paper presents a novel approach for an online initial camera calibration to estimate the extrinsic parameters for vision-based intelligent driver assistance systems. The method uses the periodicity of dashed lane markings and velocity information to determine the extrinsic camera parameters: height, pitch and roll angle. A lane marking detector is utilized to convert the images of road scenes into a set of onedimensional time series. Thereby, the lane marking detector samples the markings at predefined vertical coordinates in the image, so-called scanlines. Based on a correlation analysis and velocity information, the spatial shift between the scanlines is determined. Thus, the distances along the longitudinal lane markings are measured in the coordinate system of the vehicle independently of camera mounting parameters. The GaussNewton algorithm is implemented to minimize the squared error between these estimated distances and the distances obtained by the backprojection to a ground plane using the parameter dependent pinhole camera model. Finally, the approach is evaluated using synthetic and real data with promising results.

No abstract

No abstract