An experimental comparison of localization methods continued

IEEE International Conference on Intelligent Robots and Systems

Volumn 1, Issue , 2002, Pages 454-459

  Gutmann, Jens Steffen ^a   Fox, Dieter ^b  

^a SONY CORPORATION   (Japan)

^b University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; KALMAN FILTERING; MARKOV PROCESSES; MONTE CARLO METHODS; PROBABILITY; SENSOR DATA FUSION;

GRID-BASED MARKOV LOCALIZATION; MONTE CARLO LOCALIZATION;

MOBILE ROBOTS;

EID: 0036452994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. D. Austin and P. Jensfelt. Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization. 2000.
2. Y. Bar-Shalom and T. Fortmann. Tracking and Data Association. Academic Press. 1988.
3. J. Bruce, T. Balch, and M. Veloso. Fast and inexpensive color image segmentation for interactive robots. In Int. Conf. on Intelligent Robots and Systems (IROS), 2000.
4. W. Burgard, D. Fox, D. Hennig, and T. Schmidt. Estimating the absolute position of a mobile robot using position probability grids. In Proc. 14th National Conference on Artificial Intelligence (AAAI'96), pages 896-901, Aug. 1996.
5. Z. Crisman, E. Curre, C. Kwok, N. Ratliff, L. Tsybert, and D. Fox. Team description: UW Huskies-02. In G. Kaminka, P. Lima, and R. Rojas, editors, RoboCup-2002: Robot Soccer World Cup VI. Springer Verlag, 2003.
6. A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte Carlo in Practice. Springer-Verlag, 2001.
7. T. Duckett and U. Nehmzow. Knowing your place in real world environments. In European Workshop on Advanced Mobile Robots (EUROBOT'99), 1999.
8. D. Fox, KLD-sampling: Adaptive particle filters and mobile robot localization. In Advances in Neural Information Processing Systems (NIPS), 2001.
9. D. Fox, W. Burgard, and S. Thrun. Monte Carlo localization: Efficient position estimation for mobile robots. In Proc. National Conference on Artificial Intelligence (AAAI'99), 1999.
10. D. Fox, W. Burgard, and S. Thrun. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11, 1999.
11. D. Fox, S. Thrun, F. Dellaert, and W. Burgard. Particle filters for mobile robot localization. In Doucet et al.
12. J.-S. Gutmann. Markov-Kalman localization for mobile robots. In Int. Conf. on Pattern Recognition (ICPR), 2002.
13. J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige. An experimental comparison of localization methods. In Int. Conf. on Intelligent Robots and Systems (IROS), 1998.
14. J.-S. Gutmann, T. Weigel, and B. Nebel. A fast accurate, and robust method for self-localization in polygonal environments using laser range finders. Advanced Robotics Journal, 14(8): 651-668, 2001.
15. L. Iocchi, D. Mastrantuono, and D. Nardi. A probabilistic approach to Hough localization. In Int. Conf. on Robotics and Automation (ICRA), Seoul, Korea, 2001.
16. S. Lenser and M. Veloso. Sensor resetting localization for poorly modeled mobile robots. In Int. Conf. on Robotics and Automation (ICRA), 2000.
17. J. Leonard and H. Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transaction on Robotics and Automation, 7(3):376-382, 1991.
18. P. S. Maybeck. The Kalman filter: An introduction to concepts. In I. Cox and G. Wilfong, editors, Autonomous Robot Vehicles, pages 194-204. Springer-Verlag, 1990.
19. C. F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55-66, Feb. 2000.
20. S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128(1-2):99-141, 2000.