An improved method for fisheye camera calibration and distortion correction

Abstract

The fisheye camera has been widely studied in the field of robot vision since it can capture a wide view of the scene at one time. However, serious image distortion handers it from being widely used. To remedy this, this paper proposes an improved fisheye lens calibration and distortion correction method. First, an improved automatic detection of checkerboards is presented to avoid the original constraint and user intervention that usually existed in the conventional methods. A state-of-the-art corner detection method is evaluated and its strengths and shortcomings are analyzed. An adaptively automatic corner detection algorithm is implemented to overcome the shortcomings. Then, a precise mathematical model based on the law of fisheye lens imaging is modeled, which assumes that the imaging function can be described by a Taylor series expansion, followed by a nonlinear refinement based on the maximum likelihood criterion. With the proposed corner detection and mathematical model of fisheye imaging, both intrinsic and external parameters of the fisheye camera can be correctly calibrated. Finally, the radial distortion of the fisheye image can be corrected by incorporating the calibrated parameters. Experimental results validate the effectiveness of the proposed method.