The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B5
https://doi.org/10.5194/isprsarchives-XXXIX-B5-363-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B5-363-2012
30 Jul 2012
 | 30 Jul 2012

CALIBRATION AND EPIPOLAR GEOMETRY OF GENERIC HETEROGENOUS CAMERA SYSTEMS

A. Luber, D. Rueß, K. Manthey, and R. Reulke

Keywords: Epipolar Geometry, generic cameras, accuracy, calibration, camera model, fisheye, catadioptric

Abstract. The application of perspective camera systems in photogrammetry and computer vision is state of the art. In recent years nonperspective and especially omnidirectional camera systems were increasingly used in close-range photogrammetry tasks. In general perspective camera model, i. e. pinhole model, cannot be applied when using non-perspective camera systems. However, several camera models for different omnidirectional camera systems are proposed in literature. Using different types of cameras in a heterogeneous camera system may lead to an advantageous combination. The advantages of different camera systems, e. g. field of view and resolution, result in a new enhanced camera system. If these different kinds of cameras can be modeled, using a unified camera model, the total calibration process can be simplified. Sometimes it is not possible to give the specific camera model in advance. In these cases a generic approach is helpful. Furthermore, a simple stereo reconstruction becomes possible using a fisheye and a perspective camera for example. In this paper camera models for perspective, wide-angle and omnidirectional camera systems are evaluated. The crucial initialization of the model’s parameters is conducted using a generic method that is independent of the particular camera system. The accuracy of this generic camera calibration approach is evaluated by calibration of a dozen of real camera systems. It will be shown, that a unified method of modeling, parameter approximation and calibration of interior and exterior orientation can be applied to derive 3D object data.