东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (5): 620-624.DOI: 10.12068/j.issn.1005-3026.2017.05.003

• 信息与控制 • 上一篇    下一篇

基于畸变分离的摄像机标定方法

刘晓志1， 齐迪迪1,2， 贲驰3   

  1. (1. 东北大学 信息科学与工程学院， 辽宁 沈阳110819; 2. 海信集团有限公司， 山东 青岛266000;3. 国家电网公司东北分部， 辽宁 沈阳110180)
  • 收稿日期:2015-12-17 修回日期:2015-12-17 出版日期:2017-05-15 发布日期:2017-05-11
  • 通讯作者: 刘晓志
  • 作者简介:刘晓志(1968-)，女，辽宁沈阳人，东北大学副教授.
  • 基金资助:

    国家自然科学基金资助项目(61201054).

Camera Calibration Method Based on Distortion Separation

LIU Xiao-zhi1， QI Di-di1,2， BEN Chi3   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. Hisense Group Co.， Ltd.， Qingdao 266000， China; 3. Northeast Branch of State Grid Corporation of China， Shenyang 110180，China.
  • Received:2015-12-17 Revised:2015-12-17 Online:2017-05-15 Published:2017-05-11
  • Contact: LIU Xiao-zhi
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摘要：

在摄像机标定过程中，为了避免对摄像机模型中的畸变系数进行多次重复标定，提出一种将二阶径向畸变系数与摄像机模型分离的标定方法.该方法利用畸变形成的围线面积作为畸变评测函数，用模拟退火原理改进粒子群算法的惯性权重和学习因子；然后用改进的粒子群算法标定摄像机的畸变系数和图像中心点坐标，最后计算其他的摄像机参数.该方法无需预先知道摄像机的任何内外参数，算法简单，易于实现.实验表明，该方法与传统的非线性优化方法相比，图像坐标的平均反投影误差明显减小，而且具有更好的鲁棒性和精度.

关键词: 摄像机标定, 畸变系数, 粒子群优化算法, 畸变分离, 反投影误差

Abstract:

In the process of camera calibration， in order to avoid repeating calibration of the distortion coefficient in camera model， a distortion separated camera calibration method was proposed. The second order radial distortion was considered， in which the area of contour line formed by distortion is utilized as the criterion， and the inertia weight and learning factor of the particle swarm optimization algorithm based simulated annealing were improved. Then the improved particle swarm optimization algorithm was utilized to calibrate the distortion coefficient and principal point coordinate of the camera. Finally， the other camera parameters were calculated. The proposed method was simple and easy to implement without needing any internal and external parameters of the camera in advance. Experiments show that the proposed method has lower mean back-projection error and better robustness compared with the traditional nonlinear optimization methods.

Key words: camera calibration, distortion coefficient, particle swarm optimization algorithm, distortion separation, back-projection error

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