Cited By
View all- Leonardos SDaniilidis K(2018)A Distributed Optimization Approach to Consistent Multiway Matching2018 IEEE Conference on Decision and Control (CDC)10.1109/CDC.2018.8619511(89-96)Online publication date: 17-Dec-2018
Distributed consistent data association via permutation synchronization
Pages 2645 - 2652
Abstract
Data association is one of the fundamental problems in multi-sensor systems. Most current techniques rely on pairwise data associations which can be spurious even after the employment of outlier rejection schemes. Considering multiple pairwise associations at once significantly increases accuracy and leads to consistency. In this work, we propose a fully decentralized method for globally consistent data association from pairwise data associations based on a distributed averaging scheme on the set of doubly stochastic matrices. We demonstrate the effectiveness of the proposed method using theoretical analysis and experimental evaluation.
References
[1]
C. Zach, M. Klopschitz, and M. Pollefeys, “Disambiguating visual relations using loop constraints”, in IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[2]
A. Nguyen, M. Ben-Chen, K. Welnicka, Y. Ye, and L. Guibas, “An optimization approach to improving collections of shape maps”, Computer Graphics Forum, vol. 30, no. 5, pp. 1481–1491, 2011.
[3]
J. Yan, J. Wang, H. Zha, X. Yang, and S. Chu, “Consistency-driven alternating optimization for multigraph matching: A unified approach”, IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 994–1009, 2015.
[4]
J. Yan, M. Cho, H. Zha, X. Yang, and S. M. Chu, “Multi-graph matching via affinity optimization with graduated consistency regularization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1228–1242, 2016.
[5]
T. Zhou, Y. J. Lee, X. Y. Stella, and A. A. Efros, “Flowweb: Joint image set alignment by weaving consistent, pixel-wise correspondences”, in IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[6]
V. G. Kim, W. Li, N. J. Mitra, S. DiVerdi, and T. A. Funkhouser, “Exploring collections of 3d models using fuzzy correspondences”. ACM Transactions on Graphics, vol. 31, no. 4, p. 54, 2012.
[7]
Q.-X. Huang, G.-X. Zhang, L. Gao, S.-M. Hu, A. Butscher, and L. Guibas, “An optimization approach for extracting and encoding consistent maps in a shape collection”, ACM Transactions on Graphics, vol. 31, no. 6, p. 167, 2012.
[8]
D. Pachauri, R. Kondor, and V. Singh, “Solving the multi-way matching problem by permutation synchronization”, in Advances in Neural Information Processing Systems, 2013, pp. 1860–1868.
[9]
Q.-X. Huang and L. Guibas, “Consistent shape maps via semidefinite programming”, Computer Graphics Forum, vol. 32, no. 5, pp. 177–186, 2013.
[10]
Y. Chen, L. Guibas, and Q. Huang, “Near-optimal joint object matching via convex relaxation”, in International Conference on Machine Learning, 2014.
[11]
X. Zhou, M. Zhu, and K. Daniilidis, “Multi-image matching via fast alternating minimization”, in IEEE International Conference on Computer Vision, 2015, pp. 4032–4040.
[12]
M. Kaess and F. Dellaert, “Covariance recovery from a square root information matrix for data association” Robotics and Autonomous Systems, vol. 57, no. 12, pp. 1198–1210, 2009.
[13]
X. S. Zhou and S. I. Roumeliotis, “Multi-robot slam with unknown initial correspondence: The robot rendezvous case”, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 1785–1792.
[14]
P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes”, in Robotics-DL tentative. International Society for Optics and Photonics, 1992, pp. 586–606.
[15]
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.
[16]
J. Neira and J. D. Tardós, “Data association in stochastic mapping using the joint compatibility test”, IEEE Transactions on Robotics and Automation, vol. 17, no. 6, pp. 890–897, 2001.
[17]
T. Bailey and E. Nebot, “Localisation in large-scale environments”, Robotics and Autonomous Systems, vol. 37, no. 4, pp. 261–281, 2001.
[18]
B.-N. Vo, S. Singh, and A. Doucet, “Sequential monte carlo methods for multitarget filtering with random finite sets”, IEEE Transactions on Aerospace and electronic systems, vol. 41, no. 4, pp. 1224–1245, 2005.
[19]
A. Cunningham, K. M. Wurm, W. Burgard, and F. Dellaert, “Fully distributed scalable smoothing and mapping with robust multi-robot data association”, in Proceedings of the IEEE International Conference on Robotics and Automation, 2012, pp. 1093–1100.
[20]
S. B. Williams, G. Dissanayake, and H. Durrant-Whyte, “Towards multi-vehicle simultaneous localisation and mapping”, in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, 2002, pp. 2743–2748.
[21]
D. Fox, J. Ko, K. Konolige, B. Limketkai, D. Schulz, and B. Stewart, “Distributed multirobot exploration and mapping”, Proceedings of the IEEE, vol. 94, no. 7, pp. 1325–1339, 2006.
[22]
V. Indelman, E. Nelson, N. Michael, and F. Dellaert, “Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization”, in Proceedings of the IEEE International Conference on Robotics and Automation, 2014, pp. 593–600.
[23]
R. Aragues, E. Montijano, and C. Sagues, “Consistent data association in multi-robot systems with limited communications”, in Robotics: Science and Systems, 2011, pp. 97–104.
[24]
C. Godsil and G. F. Royle, Algebraic graph theory. Springer Science & Business Media, 2013, vol. 207.
[25]
M. Mesbahi and M. Egerstedt, Graph theoretic methods in multiagent networks. Princeton University Press, 2010.
[26]
A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules”, IEEE Transactions on Automatic Control, vol. 48, no. 6, pp. 988–1001, 2003.
[27]
R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems”, Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007.
[28]
D. P. Bertsekas and J. N. Tsitsiklis, Parallel and distributed computation: numerical methods. Prentice hall Englewood Cliffs, NJ, 1989, vol. 23.
[29]
S. Asmussen, Applied probability and queues. Springer Science & Business Media, 2008, vol. 51.
[30]
R. A. Horn and C. R. Johnson, Matrix analysis. Cambridge university press, 2012.
[31]
R. Tron and R. Vidal, “Distributed 3-d localization of camera sensor networks from 2-d image measurements”, IEEE Transactions on Automatic Control, vol. 59, no. 12, pp. 3325–3340, 2014.
[32]
H. W. Kuhn, “The hungarian method for the assignment problem”, Naval researchlogistics quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.
[33]
D. P. Bertsekas, Nonlinear programming. Athena scientific Belmont, 1999.
[34]
C. Harris and M. Stephens, “A combined corner and edge detector”. in Alvey vision conference, vol. 15. Citeseer, 1988, p. 50.
[35]
D. G. Lowe, “Distinctive image features from scale-invariant key-points”, International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
[36]
A. Vedaldi and B. Fulkerson, “Vlfeat: An open and portable library of computer vision algorithms”, in Proceedings of the 18th ACM international conference on Multimedia. ACM, 2010, pp. 1469–1472.
[37]
S. Leonardos, X. Zhou, and K. Daniilidis, “Distributed consistent data association” arXiv preprint arXiv:, 2016.
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Cited By
View all- Leonardos SDaniilidis K(2018)A Distributed Optimization Approach to Consistent Multiway Matching2018 IEEE Conference on Decision and Control (CDC)10.1109/CDC.2018.8619511(89-96)Online publication date: 17-Dec-2018