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Faculty of Science, University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2017 Vol.7(3): 93-98 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2017.7.3.668

Improving 2D Camera Calibration by LO-RANSAC

Qin Zhang, Shiqian Wu, Wei Wang, and Zhijun Fang
Abstract—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.

Index Terms—2D camera calibration; RANSAC; Lo-RANSAC.

Qin Zhang, Shiqian Wu, Wei Wang and Zhijun Fang are with School of Machinery and Automation, Wuhan University of Science and Technology, China.


Cite:Qin Zhang, Shiqian Wu, Wei Wang, and Zhijun Fang, "Improving 2D Camera Calibration by LO-RANSAC," International Journal of Information and Electronics Engineering vol. 7, no. 3, pp. 93-98, 2017.

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