A hybrid framework for mobile robot localization: Formulation using switching state-space models

Autonomous Robots

Volumn 15, Issue 2, 2003, Pages 169-191

  Baltzakis, Haris ^a,b   Trahanias, Panos ^a,b  

^a INSTITUTE OF COMPUTER SCIENCE   (Greece)

^b UNIVERSITY OF CRETE   (Greece)

Author keywords

Hidden Markov models; Hybrid models; Kalman filters; Localization; Switching state space models

Indexed keywords

AUTONOMOUS AGENTS; KALMAN FILTERING; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; STATE SPACE METHODS;

LINEAR MODELS; MARKOV MODELS; MOBILE ROBOT LOCALIZATION; STATE SPACE MODELS;

MOBILE ROBOTS;

EID: 0141973897     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1025541126280     Document Type: Article

References (29)

1. Arras, K. O.; Tomatis, N., and Siegwart, R. 2000. Multisensor on-the-fly localization using laser and vision. InProc. IEEE/RSJ International Conference Intelligent Robotics and Systems.
2. Baltzakis, H. and Trahanias, P. 2002. An iterative approach for building feature maps in cyclic environments, Accepted for presentation, IROS 2002. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems.
3. Bar-Shalom, Y. and Li, X. R. 1993. Estimation and Tracking: Principles, Techniques, and Software, Artech House Inc., Boston, MA, USA.
4. Benson, G. 1997. Sequence alignment with tandem duplication. In Annual International Conference on Computational Molecular Biology (Santa Fe, NM), ACM Press, pp. 27-36.
5. Burgard, W., Derr, A., Fox, D., and Cremers, A. B. 1998. Integrating global position estimation and position tracking for mobile robots: The Dynamic Markov Localization approach. In Proc. IEEE/RSJ International Conference Intelligent Robotics and Systems.
6. Cassandra, A. R., Kaelbling, L. P., and Kurien, J. A. 1996. Acting under uncertainty: Discrete bayesian models for mobile robot navigation. In Proc. IEEE/RSJ International Conference Intelligent Robotics and Systems.
7. Castellanos, J.A., Martínez, J.M., Neira, J., and Tardós, J.D. 1998. Simultaneous map building and localization for mobile robots: A multisensor fusion approach. In Proc. International Conference on Robotics and Automation, pp. 1244-1249.
8. Cox, I.J. and Leonard, J.J. 1994. Modeling a dynamic environment using a multiple hypothesis approach. Artificial Intelligence, 66(1): 311-344.
9. Dellaert, F., Fox, D., Burgard, W., and Thrun, F. 1999. Monte Carlo localization for mobile robots. InProc. International Conference on Robotics and Automation.
10. Duckett, T and Nehmzow, U. 2001. Mobile robot self-localisation using occupancy histograms and a mixture of gaussian location hypotheses. Robotics and Autonomous Systems Journal, 34(1/3): 119-130.
11. Duda, R.O. and Hart, P.E. 1973. Pattern Classification and Scene Analysis, Wiley-Interscience, New York.
12. Fox, D., Burgard, W., and Thrun, S. 1999. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11:391-427.
13. Ghahramani, Z. and Hinton, G. 1996. Switching state-space models. Technical Report CRG-TR-96-3, Department of Computer Science, University of Toronto.
14. Gutmann, J.-S., Burgard, W., Fox, D., and Konolige, K. 1998. An experimental comparison of localization methods. In Proc. IEEE/RSJ International Conference Intelligent Robotics and Systems.
15. Gutmann, J.-S., Weigel, T., and Nebel, B. 2001. A fast, accurate, and robust self-localization in polygonal environments using laser-range-finders. Advanced Robotics, 14(8): 651-668.
16. Jensfelt, P. and Kristensen, S. 2001. Active global localisation for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robotics and Autom, 17(5):748-760.
17. Levitt, T. and Lawton, D. 1990. Qualitative navigation for mobile robots. Artificial Intelligence, 44: 305-360.
18. Lu, F. and Milios, E.E. 1994 Robot pose estimation in unknown environments by matching 2d rangescans. CVPR, 94: 935-938.
19. Maybeck, P. 1979. Stochastic Models, Estimation and Control, Academic, New York, Vol. 1.
20. Michael, T.S. and Quint, T. 1994. Sphere of influence graphs in general metric spaces. Mathematical and Computer Modelling, 29:45-53.
21. Minka, T.P. 1999. From hidden markov models to linear dynamical systems. Technical Report TR-531, Vision and Modeling Group, MIT Media Lab.
22. Murphy, K. 1998. Learning switching kalman filter models. Technical Report 98-10, Compaq Cambridge Research Lab.
23. Nourbakhsh, I., Powers, R., and Birchfield, S. 1995. DERVISH-An office-navigating robot. AI Magazine, 16(2):53-60.
24. Roumeliotis, S.I. and Bekey, G.A. 2000. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. InProc. International Conference on Robotics and Automation, San Francisco, California, pp. 2985-2992.
25. Scott, A., Parker, L.E., and Touzet, C. 2000. Quantitative and qualitative comparison of three laser-range mapping algorithms using two types of laser scanner data. InIEEE Systems, Man, and Cybernetics Conference.
26. Simmons, R. and Koenig, S. 1995. Probabilistic robot navigation in partially observable environments. InProc. of the International Joint Conference on Artificial Intelligence, pp. 1080-1087.
27. Thiebaux, R. and Koenig, S. 2000. Combining kalman filtering and markov localization in network-like environments. In Pacific Rim International Conference on Artificial Intelligence, pp. 756-766.
28. Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Creemers, A.B., Dellaert, F., Fox, D., Hahnel, D., Rosenberg, C., Roy, N., Schulte, J., and Schulz, D. 2000. Probabilistic algorithms and the interactive museum tour-guide robot minerva. International Journal of Robotics Research, 19(11):972-999.
29. Thrun, S., Fox, D., Burgard, W., and Dellaert, F. 2001. Robust monte carlo localization for mobile robots. Artificial Intelligence, 128(1/2):99-141.