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受约束的稀疏光束法平差在相机标定中的应用

Camera Calibration Optimization with Constrained Sparse Bundle Adjustment

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摘要

直接用稀疏的光束法平差(SBA)优化张正友单相机标定算法结果会得到多组不同的相机内部参数和畸变参数(统称相机参数 )。本文在 SBA数学模型的基础之上增加了相机参数相等的约束, 建立了一种受约束的稀疏光束法平差(CSBA)模型, 提出了一种新的矩阵分块策略, 提高了稀疏线性方程组的求解效率。运用模拟实验, 验证了 CSBA算法在图像特征点像素坐标不具备零均值高斯误差时也能得到唯一的优化相机参数。最后将所提 CSBA算法应用于双目立体视觉系统, 实测实验结果表明, 所提算法能够同时优化立体视觉中的相机内外部参数并提高三维重建结果的精度。

Abstract

If Zhang’s camera calibration results are optimized with SBA directly, different sets of camera parameters (internal parameters and distortion parameters) will be obtained.Based on the mathematical model of SBA and the equality constraints of camera parameters, a Constrained Sparse Bundle Adjustment (CSBA) algorithm is proposed with a new block matrix partition strategy to improve the efficiency of solving sparse linear equations.Simulation experiments are implemented to verify that unified camera parameters can be obtained even if the pixel coordinates don’t have zero-mean Gaussian error.Finally, the CSBA algorithm is applied to a binocular stereo vision system.The experimental results demonstrate that the CSBA algorithm can optimize the camera parameters and position parameters simultaneously, and improve the accuracy of 3D reconstruction.

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DOI:10.3969/j.issn.1003-501x.2015.05.003

所属栏目:光电测量与检测

基金项目:国家自然科学基金 (51005090, 51205149)资助项目; 高等学校博士学科点专项科研基金 (20120142120006); 湖北省重大科技创新计划(2013AEA003); 材料成形与模具技术国家重点实验室自主研究项目 (2014-01)

收稿日期:2014-07-15

修改稿日期:2014-09-12

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作者单位    点击查看

夏泽民:华中科技大学材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉 430074
李中伟:华中科技大学材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉 430074
钟凯:华中科技大学材料科学与工程学院, 材料成形与模具技术国家重点实验室, 武汉 430074

联系人作者:夏泽民(465474307@qq.com)

备注:夏泽民(1989-), 男(汉族), 湖北武汉人。硕士研究生, 主要研究工作是三维测量、机器视觉。

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引用该论文

XIA Zemin,LI Zhongwei,ZHONG Kai. Camera Calibration Optimization with Constrained Sparse Bundle Adjustment[J]. Opto-Electronic Engineering, 2015, 42(5): 13-19

夏泽民,李中伟,钟凯. 受约束的稀疏光束法平差在相机标定中的应用[J]. 光电工程, 2015, 42(5): 13-19

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