Improving 2D Camera Calibration by LO-RANSAC
Keywords:
2D camera calibration; RANSAC; Lo RANSACAbstract
The performance of the traditional 2D pattern
based camera calibration is affected by sample viewpoints, positions and feature point localization. In this paper, the Locally Optimized RANSAC (Random Sample Consensus) (LO-RANSAC) is employed to remove the unreliable information automatically. To be more particular, a distance between a specific circular point and the underlying image of the absolute conic is adopted, and a local optimization is achieved when the so-far-best model in the RANSAC iterations has been reached. The experiments on artificial and real data
demonstrate that the proposed method alleviates the
randomness of the RANSAC solution and get more accurate and reliable calibration results than the traditional methods.
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