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基于畸变模型的双目相机高精度标定方法。

High-precision method of binocular camera calibration with a distortion model.

作者信息

Li Weimin, Shan Siyu, Liu Hui

出版信息

Appl Opt. 2017 Mar 10;56(8):2368-2377. doi: 10.1364/AO.56.002368.

DOI:10.1364/AO.56.002368
PMID:28375284
Abstract

A high-precision camera calibration method for binocular stereo vision system based on a multi-view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e-6 and 1.2e-6 for the focal length and the principal point, respectively, under zero-mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e-6 over the intrinsic parameters. The proposed method is convenient, cost-efficient, highly precise, and simple to carry out.

摘要

本文提出了一种基于多视图模板和交替光束平差的双目立体视觉系统高精度相机标定方法。该方法通过在一个专门设计的标定模板上拍摄多张照片来实现,该模板在不同方向上有多种编码点。本文利用现有的单目相机标定算法来获取初始化,其中涉及一个相机模型,包括径向镜头畸变和切向畸变。我们基于左相机坐标创建一个参考坐标系,通过交替光束平差来优化左相机的内参以获得最优值。然后,当我们基于右相机坐标创建一个参考坐标系时,通过交替光束平差可以获得右相机的最优内参。我们还使用获取的所有内参来优化外参。从而获得最优的镜头畸变参数以及内参和外参。使用合成数据和真实数据对该方法进行测试。仿真结果表明,在标准差为0.05像素的零均值高斯噪声下,焦距和主点的最大平均绝对相对标定误差分别约为3.5e - 6和1.2e - 6。实际结果表明,我们模型的重投影误差约为0.045像素,内参的相对标准差为1.0e - 6。所提出的方法方便、经济高效、精度高且易于实施。

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