Fast and robust fundamental matrix estimation based on SSDA

TANG Yong-he; HU Xu-feng; LU Huan-zhang

doi:10.3788/OPE.20111911.2759

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Fast and robust fundamental matrix estimation based on SSDA

Article | 更新时间：2020-08-12

- Fast and robust fundamental matrix estimation based on SSDA
- Optics and Precision Engineering Vol. 19, Issue 11, Pages: 2759-2766(2011)
- 作者机构：
  
  国防科学技术大学 ATR实验室,湖南长沙,410073
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111911.2759
  CLC： TP391
- Received：09 May 2011，
  
  Revised：22 June 2011，
  
  Published Online：25 November 2011，
  
  Published：25 November 2011
- 稿件说明：
移动端阅览
唐永鹤, 胡旭峰, 卢焕章. 应用序贯相似检测的基本矩阵快速鲁棒估计[J]. 光学精密工程, 2011,19(11): 2759-2766

TANG Yong-he, HU Xu-feng, LU Huan-zhang. Fast and robust fundamental matrix estimation based on SSDA[J]. Editorial Office of Optics and Precision Engineering, 2011,19(11): 2759-2766
唐永鹤, 胡旭峰, 卢焕章. 应用序贯相似检测的基本矩阵快速鲁棒估计[J]. 光学精密工程, 2011,19(11): 2759-2766 DOI： 10.3788/OPE.20111911.2759.

TANG Yong-he, HU Xu-feng, LU Huan-zhang. Fast and robust fundamental matrix estimation based on SSDA[J]. Editorial Office of Optics and Precision Engineering, 2011,19(11): 2759-2766 DOI： 10.3788/OPE.20111911.2759.

摘要

提出了一种基于序贯相似检测(SSDA)的快速鲁棒基本矩阵估计算法来估计基本矩阵。在最大后验一致性(MAPSAC)算法中引入SSDA搜索最优模型参数

通过及时剔除错误模型减少计算成本函数的累加次数

不仅保持了MAPSAC的良好鲁棒性

而且有效减少了算法的计算量。用M估计算法对改进的MAPSAC算法获得的初始内点集进行优化

剔除估计余差较大的内点

并用优化的内点集求解基本矩阵

进一步提高算法的估计精度和鲁棒性。实验结果表明

该算法不仅估计精度较高

鲁棒性较好

而且平均处理速度比MAPSAC算法提高了30%以上

基本满足三维重建、匹配和跟踪、相机自标定等应用领域对实时性、鲁棒性和精度的要求。

Abstract

A fast and robust fundamental matrix estimation method based on Sequential Similarity Detection Algorithm (SSDA) is presented to estimate the fundamental matrix rapidly and accurately. The SSDA is introduced into the Maximum a Posteriori Sample Consensus (MAPSAC) to search the optimum model parameters and the accumulation times of computing a cost function are cut down by eliminating the false model as soon as possible

which not only keeps the better robustness of MAPSAC

but also reduces its computation effectively. Then

the initial inliers obtained by the improved MAPSAC are optimized with a M-estimator. Those inliers with larger residual errors are removed and the optimized inliers are used to compute the fundamental matrix to enhance the precision and improve the robustness of the algorithm. Experiment results demonstrate that the proposed algorithm performs better in accuracy and robustness

and its average speed has increased at least 30% as compared with that of the MAPSAC. The proposed algorithm can satisfy the requirements for real-time

precision and robustness in the fields such as three-dimensional reconstruction

image matching

image tracking and camera self-calibration.

关键词

Keywords

references

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