A multifrontal QR factorization approach to distributed inference applied to multirobot localization and mapping

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2005, Pages 1261-1266

  Dellaert, Frank ^a   Kipp, Alexander ^a   Krauthausen, Peter ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FACTOR GRAPHS; MULTIROBOT LOCALIZATION; QR FACTORIZATION; SUBTREES;

ALGORITHMS; COMPUTATION THEORY; COMPUTER SCIENCE; CONFORMAL MAPPING; INFORMATION TECHNOLOGY; MATHEMATICAL MODELS; ROBOTS; TREES (MATHEMATICS);

LINEAR ALGEBRA;

EID: 29344471073     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Amestoy, P. R.; Duff, I. S.; and Puglisi, C. 1994. Multifrontal QR factorization in a multiprocessor environment. Technical Report TR/PA/94/09, ENSEEIHT, Toulouse, France.
2. Blair, J., and Peyton, B. 1993. An introduction to chordal graphs and clique trees. In George, J.; Gilbert, J.; and Liu, J.-H., eds., Graph Theory and Sparse Matrix Computations. Springer-Verlag.
3. Cowell, R. G.; Dawid, A. P.; Lauritzen, S. L.; and Spiegelhalter, D. J. 1999. Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Science. Springer-Verlag.
4. Dellaert, F.; Alegre, F.; and Martinson, E. 2003. Intrinsic localization and mapping with 2 applications: Diffusion mapping and Marco Polo localization. In IEEE Intl. Conf. on Robotics and Automation (ICRA).
5. Golub, G., and Loan, C. V. 1996. Matrix Computations. Baltimore: Johns Hopkins University Press, third edition.
6. Guestrin, C.; Bodik, P.; Thibaux, R.; Paskin, M.; and Madden, S. 2004. Distributed regression: an efficient framework for modeling sensor network data. In Information Processing in Sensor Networks (IPSN).
7. Howard, A.; Matarić, M.; and Sukhatme, G. 2002. Localization for mobile robot teams using maximum likelihood estimation. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 434-459.
8. Howard, A.; Matarić, M.; and Sukhatme, G. 2003. Localization for mobile robot teams: A distributed MLE approach. In Siciliano, B., and Dario, P., eds., Experimental Robotics VIII, Advanced Robotics Series. Springer-Verlag. 146-155.
9. Kschischang, F.; Frey, B.; and Loeliger, H.-A. 2001. Factor graphs and the sum-product algorithm. IEEE Trans. Inform. Theory 47(2).
10. Leonard, J.; Cox, I.; and Durrant-Whyte, H. 1992. Dynamic map building for an autonomous mobile robot. Intl. J. of Robotics Research 11(4):286-289.
11. Lu, S.-M., and Barlow, J. L. 1996. Multifrontal computation with the orthogonal factors of sparse matrices. SIAM Journal on Matrix Analysis and Applications 17(3):658-679.
12. Lu, F., and Milios, E. 1997. Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems 249:275.
13. Matstoms, P. 1994. Sparse QR factorization in MATLAB. ACM Trans. Math. Softw. 20(1): 136-159.
14. Paskin, M. A., and Guestrin, C. E. 2004. Robust probabilistic inference in distributed systems. In Conf. on Uncertainty in Artificial Intelligence.
15. Paskin, M.; Guestrin, C.; and McFadden, J. 2005. A robust architecture for distributed inference in sensor networks. In Fourth International Conference on Information Processing in Sensor Networks, ISPN.
16. Pothen, A., and Sun, C. 1992. Distributed multifrontal factorization using clique trees. In Proceedings of the Fifth SIAM Conference on Parallel Processing for Scientific Computing, 34-40. Society for Industrial and Applied Mathematics.
17. Smith, R.; Self, M.; and Cheeseman, P. 1990. Estimating uncertain spatial relationships in Robotics. In Cox, I., and Wilfong, G., eds., Autonomous Robot Vehicles. Springer-Verlag. 167-193.
18. Thrun, S. 2001. A probabilistic online mapping algorithm for teams of mobile robots. Intl. J. of Robotics Research 20(5):335-363.
19. Triggs, B.; McLauchlan, P.; Hartley, R.; and Fitzgibbon, A. 2000. Bundle adjustment - a modern synthesis. In Triggs, W.; Zisserman, A.; and Szeliski, R., eds., Vision Algorithms: Theory and Practice, LNCS, 298-375. Springer Verlag.
20. Yedidia, J.; Freeman, W.; and Y. Weiss. 2000. Generalized belief propagation. In Advances in Neural Information Processing Systems (NIPS), 689-695.