Towards the intrinsic self-calibration of a vehicle-mounted omni-directional radially symmetric camera

Intrinsic calibration, i.e. finding the mapping between a camera's image positions and corresponding view rays, is a cumbersome, yet unavoidable task in order to accurately generate and interpret results from many kinds of image processing algorithms. We address this problem in the context of vehicle-mounted cameras with arbitrary fields of view with applications in advanced driver assistance systems. In particular, we present algorithms to gather the necessary data from unknown scenes and to subsequently estimate the camera parameters. These do rely on vehicle odometry only to resolve the focal scale ambiguity and to recognize when a purely translational motion is performed. We pay special attention to noise handling and circumvention of numerical instabilities. The proposed pipeline is tested by means of simulations to examine its noise sensitivity. Additionally we calibrate a fisheye camera from a natural scene of only 14 seconds length. First results show that the self-calibration in natural scenes is eligible and outperforms the straightforward approach of using all calibration parameters from an identically constructed camera.