A novel approach for the online initial calibration of extrinsic parameters for a car-mounted camera

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 one-dimensional 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 Gauss-Newton 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.