Abstract
The fish-eye lens camera has a wide field of view that makes it effective for various applications and sensor systems. However, it incurs strong geometric distortion in the image due to compressive recording of the outer part of the image. Such distortion must be interpreted accurately through a self-calibration procedure. This paper proposes a new type of test-bed (the AV-type test-bed) that can effect a balanced distribution of image points and a low level of correlation between orientation parameters. The effectiveness of the proposed test-bed in the process of camera self-calibration was verified through the analysis of experimental results from both a simulation and real datasets. In the simulation experiments, the self-calibration procedures were performed using the proposed test-bed, four different projection models, and five different datasets. For all of the cases, the Root Mean Square residuals (RMS-residuals) of the experiments were lower than one-half pixel. The real experiments, meanwhile, were carried out using two different cameras and five different datasets. These results showed high levels of calibration accuracy (i.e., lower than the minimum value of RMS-residuals: 0.39 pixels). Based on the above analyses, we were able to verify the effectiveness of the proposed AV-type test-bed in the process of camera self-calibration.
Highlights
Fish-eye lens cameras have been used in various fields including indoor and outdoor 3D modeling, autonomous driving, augmented and virtual reality, Simultaneous Localization And Mapping (SLAM), people- and motion-detection, and so on
Perez-Yus et al [6] overcame the narrow Field Of View (FOV) of an RGB-depth system by coupling a fish-eye camera to it, and Sreedhar et al [7] proposed a system for Virtual Reality (VR) applications using multiple fish-eye lenses mounted on a camera system covering the entire 360-degree FOV
The simulation-based experiments were analyzed in two steps: the first step evaluated the stability and correlation of the solution; the second step analyzed the accuracies of the estimated Interior Orientation Parameters (IOPs)
Summary
Fish-eye lens cameras have been used in various fields including indoor and outdoor 3D modeling, autonomous driving, augmented and virtual reality, Simultaneous Localization And Mapping (SLAM), people- and motion-detection, and so on. This camera has a wider Field Of View (FOV) relative to a conventional optical camera; it can record an image over a much wider area. Zia et al [4] proposed a UAV sensor system to produce a high-quality 360-degree panorama, and used a fish-eye lens camera for securing sufficient overlap between adjacent camera views. Xu et al [9] used a fish-eye lens camera for production of a 3D motion capture system, and Krams et al [10] used a fish-eye lens camera for people-detection purposes
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