计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1706-1710.DOI: 10.11772/j.issn.1001-9081.2014.06.1706

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于直接解算与迭代优化的相对定向方法

杨阿华1,李学军2,刘涛2,李东岳3   

  1. 1. 装备学院 研究生管理大队,北京 10141
    2. 装备学院 信息装备系,北京 101416;
    3. 63628部队,河北 廊坊 065201
  • 收稿日期:2013-11-25 修回日期:2014-01-13 出版日期:2014-06-01 发布日期:2014-07-02
  • 通讯作者: 杨阿华
  • 作者简介:杨阿华(1985-),男, 湖南湘乡人,博士研究生,主要研究方向:计算机图形图像处理、计算机视觉;李学军(1967-),男,湖北监利人,教授,博士,主要研究方向:计算机图形图像处理;刘涛(1979-),男,陕西汉中人,副教授,博士,主要研究方向:数据库系统;李东岳(1982-),女,山东泰安人,工程师,硕士,主要研究方向:计算机图形学。
  • 基金资助:

    国防预研基金资助项目;总装备部国防预研项目

Relative orientation approach based on direct resolving and iterative refinement

YANG Ahua1,LI Xuejun2,LIU Tao2,LI Dongyue3   

  1. 1. Department of Graduate Management, Equipment Academy, Beijing 101416, China;
    2. Department of Information Equipment, Equipment Academy, Beijing 101416, China;
    3. 63628 Troops, Hebei Langfang 065201, China
  • Received:2013-11-25 Revised:2014-01-13 Online:2014-06-01 Published:2014-07-02
  • Contact: YANG Ahua

摘要:

为了提高相对定向的鲁棒性和精度,提出了一种直接解算与迭代优化相结合的相对定向方法。该方法首先由同名点估计本征矩阵;然后,通过分解本征矩阵得到两相机的初始相对位姿,详细介绍了确定唯一初始位姿参数的过程;最后,通过建立水平核线坐标系,基于共面约束由同名点构建约束方程组,对初始位姿参数进行迭代优化。通过在直接解算时采用随机采样一致性(RANSAC)策略及迭代优化中进行动态剔点,使算法对外点具有极高的抗性。仿真实验结果表明,在引入各种随机误差的条件下,所提方法的解算效率和精度均优于传统方法。实际数据实验证明所提算法可有效应用于三维重建中的相对位姿估计。

Abstract:

In order to improve the robustness and accuracy of relative orientation, an approach combining direct resolving and iterative refinement for relative orientation was proposed. Firstly, the essential matrix was estimated from some corresponding points. Afterwards the initial relative position and posture of two cameras were obtained by decomposing the essential matrix. The process for determining the only position and posture parameters were introduced in detail. Finally, by constructing the horizontal epipolar coordinate system, the constraint equation group was built up from the corresponding points based on the coplanar constraint, and the initial position and posture parameters were refined iteratively. The algorithm was resistant to the outliers by applying the RANdom Sample Consensus (RANSAC) strategy and dynamically removing outliers during iterative refinement. The simulation experiments illustrate the resolving efficiency and accuracy of the proposed algorithm outperforms that of the traditional algorithm under the circumstance of importing varies of random errors. And the experiment with real data demonstrates the algorithm can be effectively applied to relative position and posture estimation in 3D reconstruction.

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