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Abstract:

In recent years, fisheye cameras have been widely used in various fields due to their ultra-wide field of view, but they are prone to image distortion that affects application, requiring calibration. Traditional calibration methods are time-consuming and cumbersome when capturing multiple images continuously, and the conventional KB(Kannala-Branolt) model fails to project severely distorted feature points of fisheye cameras with a field of view exceeding 180°. To address these issues, a stereo calibration box for large-wide-angle fisheye lenses devised and an adaptive Christopher Mei model is used for one-shot calibration. The calibration box is designed, then a centroid positioning method is used for precise feature point extraction, and finally MEI calibration is enhanced with the particle swarm optimization algorithm. Experiments show that this method is stable, accurate, and robust, reducing time consumption and improving accuracy compared to traditional methods.

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