Self-Calibrating Network Analysis for Panoramic Cameras by Heuristic Simulation

Abstract

Linear array CCD-based panoramic cameras have a high potential for measurement applications due to their design in acquiring 360 degree field of views and high information content with up to a Giga-pixel image data in one scan. The best possible accuracy of such a system can be obtained by a suitable sensor model and by establishing an optimal network following the concept of network design. The influence of different network configurations on the object point coordinates precision was shown in our previous studies with networks of panoramic cameras and panoramic and matrix array CCD cameras. In this paper, the influence of different network configurations onto the determination of Additional Parameters (APs) for self-calibration is demonstrated. The accuracy and precision values of object points and the correlations of APs with respect to the object point coordinates and the exterior orientation parameters are analyzed. By computer simulation and some sensor assumptions, networks of leveled and tilted panoramic camera stations, at the same and at different heights, are analyzed. The datum choice in all network cases is the “free network”, based on the concept of inner constraints. We show that by increasing the tilt of camera stations the correlations of parameters are decreasing, especially the correlations of APs with object space coordinates. Based on these results we suggest tilted panoramic camera stations for the purpose of self-calibration.