[1] M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981. doi: 10.1145/358669.358692
[2] P. J. Rousseeuw, “Least median of squares regression,” J. American Statistical Association, vol. 79, no. 388, pp. 871–880, 1984. doi: 10.1080/01621459.1984.10477105
[3] D. Nistér, “An efficient solution to the five-point relative pose problem,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 756–770, 2004. doi: 10.1109/TPAMI.2004.17
[4] “OpenCV 3.1: Open source computer vision library,” [Online]. Available: https://github.com/opencv/opencv/releases/tag/3.1.0, 2015.
[5] Y. Ma, J. Košecká, and S. Sastry, “Optimization criteria and geometric algorithms for motion and structure estimation,” Int. J. Computer Vision, vol. 44, no. 3, pp. 219–249, 2001. doi: 10.1023/A:1012276232049
[6] T. Botterill, S. Mills, and R. Green, “Refining essential matrix estimates from RANSAC,” in Proc. Image and Vision Computing New Zealand, 2011, pp. 1–6.
[7] T. Botterill, S. Mills, and R. Green, “Fast RANSAC hypothesis generation for essential matrix estimation,” in Proc. IEEE Int. Conf. Digital Image Computing Techniques and Applications, 2011, pp. 561–566.
[8] U. Helmke, K. Hüper, P. Y. Lee, and J. Moore, “Essential matrix estimation using Gauss-Newton iterations on a manifold,” Int. J. Computer Vision, vol. 74, no. 2, pp. 117–136, 2007. doi: 10.1007/s11263-006-0005-0
[9] M. Sarkis, K. Diepold, and K. Hüper, “A fast and robust solution to the five-point relative pose problem using Gauss-Newton optimization on a manifold,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2007.
[10] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cabridge University Press, 2003.
[11] J. Shi and Tomasi, “Good features to track,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1994.
[12] S. Baker and I. Matthews, “Lucas-Kanade 20 years on: A unifying framework,” Int. J. Computer Vision, vol. 56, no. 3, pp. 221–255, 2004. doi: 10.1023/B:VISI.0000011205.11775.fd
[13] S. Belongie, “Cse 252b: Computer vision II, lecture 11,” [Online]. Available: https://cseweb.ucsd.edu/classes/sp04/cse252b/notes/lec11/lec11.pdf, 2006.
[14] C. Hertzberg, R. Wagner, U. Frese, and L. Schröder, “Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds,” Information Fusion, vol. 14, no. 1, pp. 57–77, 2013. doi: 10.1016/j.inffus.2011.08.003
[15] C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “On-manifold preintegration for real-time visual-inertial odometry,” IEEE Trans. Robotics, vol. 33, no. 1, pp. 1–21, Fe. 2017. doi: 10.1109/TRO.2016.2597321
[16] P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection. John Wiley & Sons, 2005, vol. 589.
[17] V. Santhaseelan and V. K. Asari, “Moving object detection and tracking in wide area motion imagery,” in Wide Area Surveillance. Springer, 2014, pp. 49–70.
[18] Y. Sheikh, O. Javed, and T. Kanade, “Background subtraction for freely moving cameras,” in Proc. IEEE 12th Int. Conf. Computer Vision, 2009, pp. 1219–1225.
[19] A. Elqursh and A. Elgammal, “Online moving camera background subtraction,” in Proc. European Conf. Computer Vision. Springer, 2012, pp. 228–241.
[20] J. Kang, I. Cohen, G. Medioni, and C. Yuan, “Detection and tracking of moving objects from a moving platform in presence of strong parallax,” in Proc. 10th IEEE Int. Conf. Computer Vision, vol. 1, pp. 10–17, 2005.
[21] S. Dey, V. Reilly, I. Saleemi, and M. Shah, “Detection of independently moving objects in non-planar scenes via multi-frame monocular epipolar constraint,” in Proc. 12th European Conf. Computer Vision, vol. 7576, pp. 860–873, 2012.
[22] P. C. Niedfeldt and R. W. Beard, “Multiple target tracking using recursive RANSAC,” in Proc. American Control Conf., Jun. 2014, pp. 3393–3398.
[23] P. C. Niedfeldt, K. Ingersoll, and R. W. Beard, “Comparison and analysis of recursive-RANSAC for multiple target tracking,” IEEE Trans Aerospace and Electronic Systems, vol. 53, no. 1, pp. 461–476, Feb. 2017. doi: 10.1109/TAES.2017.2650818
[24] Y. Bar-Shalom, F. Daum, and J. Huang, “The probabilistic data association filter,” IEEE Control Systems, vol. 29, no. 6, 2009.
[25] “BYU holodeck: A high-fidelity simulator for deep reinforcement learning,” [Online]. Available: https://github.com/byu-pccl/holodeck, 2018.