快速鲁棒的基础矩阵估计

针对基础矩阵估计过程中因受野值影响导致估计精度下降和稳定性不高等问题，本文提出了一种新的快速鲁棒的基础矩阵估计方法。该方法首先将野值去除融入到计算基础矩阵的过程中，而不再将它作为一个独立的处理步骤。通过迭代将潜在的错误对应点剔除，从而实现基础矩阵的稳定估计。然后，在每次迭代过程中，采用对极几何误差准则来识别野值，同时获得基础矩阵的估计结果。该迭代过程收敛较快，即使存在大量匹配野值的情况下，计算值也会很快趋于稳定。仿真和实际实验结果一致表明：所提出的算法在保证类似估计精度的同时还在计算效率方面有极大地提升，相比较快的M估计法有30%以上的速度提升，而相比于估计精度较优的MAPSAC算法甚至达到4倍以上。

Abstract

In this paper, a new fast and robust fundamental matrix estimation method was proposed to solve the problem that the estimation of fundamental matrix leads to lower estimation accuracy and lower stability due to outliers. The method removed outliers into the computation of the fundamental matrix instead of taking it as an independent processing step. The potential error corresponding points were eliminated by iteration to achieve the stable estimation of the fundamental matrix. Then, the epipolar geometry error criterion was used to identify outliers and the estimation results of the fundamental matrix were obtained during each iteration. The iterative process could converge quickly, even if a large number of matched outliers were present, the calculated values would soon become stable. The results of simulation and actual experimental show that the proposed algorithm improves the estimation accuracy greatly, and also ensures similar calculation efficiency at the same time. Compared with the method of M-estimator, it has more than 30% speed improvement, and compared with the MAP-SAC algorithm with higher estimation accuracy, it even achieves more than 4 times.

参考文献

[1] 向长波, 谢丹, 刘太辉, 等. 估计多视点摄像机姿态的两步法［J］. 光学精密工程, 2008, 16(10): 1982-1987.

XIANG CH B, XIE D, LIU T H, et al.. A two-step algorithm for estimating postures of cameras located in different points of view［J］. Opt. Precision Eng., 2008, 16(10): 1982-1987. (in Chinese)

[2] 张灵飞, 陈刚, 叶东, 等. 用自由移动的刚性球杆校准多摄像机内外参数［J］. 光学精密工程, 2009, 17(8): 1942-1952.

ZHANG L F, CHEN G, YE D, et al.. Calibrating internal and external parameters of multi-cameras by moving freely rigid ball bar［J］. Opt. Precision Eng., 2009, 17(8): 1942-1952. (in Chinese)

[3] 江剑鸣, 闫志杰, 段晓杰, 等. 相机自运动参数的鲁棒性估计［J］. 红外与激光工程, 2010, 39(6): 1168-1172.

WANG J M, YAN ZH J, DUAN X J, et al.. Robust estimation of camera ego-motion parameters［J］. Infrared and Laser Engineering, 2010, 39(6): 1168-1172. (in Chinese)

[4] LONGUET-HIGGINS H S. A computer algorithm for reconstructing a scene from two projections［M］. Fischler M A, Firschein O, eds. Readings in Computer Vision. Amsterdam: Elsevier Inc, 1981: 133-135.

[5] TORR P H S, MURRAY D W. The development and comparison of robust methods for estimating the fundamental matrix［J］. International Journal of Computer Vision, 1997, 24(3): 271-300.

[6] ARMANGU X, SALVI J. Overall view regarding fundamental matrix estimation［J］. Image and Vision Computing, 2003, 21(2):205-220.

[7] HARTLEY R I. In defense of the 8-point algorithm［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(6): 580-593.

[8] FATHY M E, HUSSEIN A S, TOLBA M F. Fundamental matrix estimation: A study of error criteria［J］. Pattern Recognition Letters, 2011, 32(2): 383-391.

[9] 陈付幸, 王润生. 基于预检验的快速随机抽样一致性算法［J］. 软件学报, 2005, 16(8): 1431-1437.

CHEN F X, WANG R SH. Fast RANSAC with preview model parameters evaluation［J］. Journal of Software, 2005, 16(8): 1431-1437. (in Chinese)

[10] TORR P H S, ZISSERMAN A. MLESAC: a new robust estimator with application to estimating image geometry［J］. Computer Vision and Image Understanding, 2010, 78(1): 138-156.

[11] TORR P H S. Bayesian model estimation and selection for epipolar geometry and generic manifold fitting［J］. International Journal of Computer Vision, 2002, 50(1): 35-61.

[12] 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 Y H, HU X F, LU H ZH. Fast and robust fundamental matrix estimation based on SSDA［J］. Opt. Precision Eng., 2011, 19(11): 2759-2766. (in Chinese)

[13] 黄春燕, 韩燮, 韩慧妍, 等. 一种改进的基础矩阵估计算法［J］. 小型微型计算机系统, 2014, 35(11): 2578-2581.

HUANG CH Y, HAN X, HAN H Y, et al.. Improved fundamental matrix estimation algorithm［J］. Journal of Chinese Computer Systems, 2014, 35(11): 2578-2581. (in Chinese)

[14] 张永祥, 古佩强, 穆铁英. 改进的RANSAC基础矩阵估计算法［J］. 小型微型计算机系统, 2016, 37(9): 2084-2087.

ZHANG Y X, GU P Q, MU T Y. Improved RANSAC algorithm for fundamental matrix estimation［J］. Journal of Chinese Computer Systems, 2016, 37(9): 2084-2087. (in Chinese)

[15] ZHOU F, ZHONG C, ZHENG Q. Method for fundamental matrix estimation combined with feature lines［J］. Neurocomputing, 2015, 160: 300-307.

[16] 张永祥, 穆铁英, 张伟功, 等. 一种新的估计基础矩阵的高精度鲁棒算法［J］. 微电子学与计算机, 2016, 33(3): 32-36.

ZHANG Y X, MU T Y, ZHANG W G, et al.. A new fundamental matrix estimation algorithm of high accuracy and robustness［J］. Microelectronics＆Computer, 2016, 33(3): 32-36. (in Chinese)

[17] BUGARIN F, BARTOLI A, HENRION D, et al.. Rank-constrained fundamental matrix estimation by polynomial global optimization versus the eight-point algorithm［J］. Journal of Mathematical Imaging and Vision, 2015, 53(1): 42-60.

[18] ZHENG Y Q, SUGIMOTO S, OKUTOMI M. A practical rank-constrained eight-point algorithm for fundamental matrix estimation［C］. Conference in Computer Vision and Pattern Recognition, IEEE, 2013, 9: 1546-1553.

[19] ZHANG Z Y, DERICHE R, FAUGERAS O, et al.. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry［J］. Artificial Intelligence, 1995, 78(1-2): 87-119.

颜坤, 刘恩海, 赵汝进, 田宏, 张壮. 快速鲁棒的基础矩阵估计[J]. 光学精密工程, 2018, 26(2): 461. YAN Kun, LIU En-hai, ZHAO Ru-jin, TIAN Hong, ZHANG Zhuang. A fast and robust method for fundamental matrix estimation[J]. Optics and Precision Engineering, 2018, 26(2): 461.

快速鲁棒的基础矩阵估计

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