Bayesian Landmark Learning for Mobile Robot Localization

Machine Learning

Volumn 33, Issue 1, 1998, Pages 41-76

  Thrun, Sebastian ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Artificial neural networks; Bayesian analysis; Feature extraction; Landmarks; Localization; Mobile robots; Positioning

Indexed keywords

FEATURE EXTRACTION; INTELLIGENT ROBOTS; LEARNING ALGORITHMS; MOBILE ROBOTS; MOTION PLANNING; NEURAL NETWORKS; OPTIMIZATION; PROBABILITY; SENSOR DATA FUSION; STATISTICAL METHODS; ALGORITHMS; COMPUTER AIDED DESIGN; MATHEMATICAL MODELS; SENSORS;

BAYESIAN LANDMARK LEARNING;

ROBOT LEARNING; MOBILE ROBOTS;

BAYESIAN ANALYSIS; LANDMARKS; LOCALIZATION; POSITIONING;

EID: 0032182830     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007554531242     Document Type: Article

References (69)

1. Betke, M., & Gurvits, L. (1993). Mobile robot localization using landmarks (Technical Report SCR-94-TR-474). Princeton: Siemens Corporate Research.
2. Borenstein, J. (1987). The nursing robot system. Doctoral dissertation, Technion, Haifa, Israel.
3. Borenstein, J., Everett, B., & Feng, L. (1996). Navigating mobile robots: systems and techniques. Wellesley, MA: A. K. Peters, Ltd.
4. Buhmann, J. (1995). Data clustering and learning. In M. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 278-282). Cambridge, MA: Bradfort Books/MIT Press.
5. Buhmann, J., Burgard, W., Cremers, A.B., Fox, D., Hofmann, T., Schneider, F, Strikos, J., & Thrun, S. (1995). The mobile robot Rhino. AI Magazine, 16, 31-38.
6. Burgard, W., Fox, D., Hennig, D., & Schmidt, T. (1996a). Estimating the absolute position of a mobile robot using position probability grids. Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 896-901). Menlo Park, CA: AAAI Press/MIT Press.
7. Burgard, W., Fox, D., Hennig, D., & Schmidt, T. (1996b). Position tracking with position probability grids. Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (pp. 2-9). IEEE Computer Society Press.
8. Burgard, W., Fox, D., & Thrun, S. (1997). Active mobile robot localization. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 1346-1352). San Francisco, CA: Morgan Kaufmann.
9. Casella, G., & Berger, R. (1990). Statistical Inference. Pacific Grove, CA: Wadsworth & Brooks.
10. Chatila, R., & Laumond, J.-P. (1985). Position referencing and consistent world modeling for mobile robots. Proceedings of the 1985 IEEE International Conference on Robotics and Automation (pp. 138-145).
11. Chown, E., Kaplan, S., & Kortenkamp, D. (1995). Prototypes, location, and associative networks (PLAN): Towards a unified theory of cognitive mapping. Cognitive Science, 19, 1-51.
12. Chung, K. (1960). Markov chains with stationary transition probabilities. Berlin, Germany: Springer Publisher.
13. Collet, T., & Cartwright, B. (1985). Landmark learning in bees. Journal of Comparative Physiology.
14. Cox, I. (1991). Blanche - An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, 7, 193-204.
15. Cox, I, (1994). Modeling a dynamic environment using a Bayesian multiple hypothesis approach. Artificial Intelligence, 66, 311-344.
16. Engelson, S. (1994). Passive map laming and visual place recognition. Doctoral dissertation, Department of Computer Science, Yale University.
17. Everett, H., Gage, D., Gilbreth, G., Laird, R., & Smurlo, R. (1994). Real-world issues in warehouse navigation. Proceedings of the SPIE Conference on Mobile Robots IX. Volume 2352. Boston, MA.
18. Fahlman, S.E., & Lebiere, C. (1989). Cascade-correlation learning architecture (Technical Report CMU-CS-90-100). Pittsburgh, PA: Carnegie Mellon University, Computer Science Department.
19. Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine, 4(1), 23-33.
20. Franke, R. (1982). Scattered data interpolation: Tests of some methods. Mathematics of Computation, 38(157), 181-200.
21. Fukuda, T., Ito, S., Oota, N., Arai, F., Abe, Y., Tanake, K., & Tanaka, Y. (1993). Navigation system based on ceiling landmark recognition for autonomous mobile robot. Proceedings of the International Conference on Industrial Electronics Control and Instrumentation, (Vol. 1, pp. 1466-1471).
22. Gelb, A. (1974). Applied optimal estimation. Cambridge, MA: MIT Press.
23. Greiner, R., & Isukapalli, R. (1994). Learning to select useful landmarks. Proceedings of National Conference on Artificial Intelligence (pp. 1251-1256). Menlo Park, CA: AAAI Press/The MIT Press.
24. Hertz, J., Krogh, A., & Palmer, R.G. (1991). Introduction to the theory of neural computation. Redwood City, CA: Addison-Wesley.
25. Hinkel, R., & Knieriemen, T. (1988). Environment perception with a laser radar in a fast moving robot. Proceedings of Symposium on Robot Control (pp. 68.1-68.7). Karlsruhe, Germany.
26. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359-366.
27. Horswill, I. (1994). Specialization of perceptual processes (Technical Report AITR-1511). Cambridge, MA: MIT, Artificial Intelligence Laboratory.
28. Howard, R.A. (1960). Dynamic Programming and Markov Processes. Cambridge, MA: MIT Press and Wiley.
29. Kaelbling, L., Cassandra, A., & Kurien, J. (1996). Acting under uncertainty: Discrete bayesian models for mobile-robot navigation. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 963-972).
30. Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Transactions ASME, Journal of Basic Engineering, 82, 35-45.
31. King, S., & Weiman, C. (1990). Helpmate autonomous mobile robot navigation system. Proceedings of the SPIE Conference on Mobile Robots (Vol. 2352, pp. 190-198). Boston, MA.
32. Koenig, S., & Simmons, R. (1996). Passive distance learning for robot navigation. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 266-274). San Mateo, CA: Morgan Kaufmann.
33. Kortenkamp, D., Bonassi, D.P., & Murphy, R. (Eds.) (in press). AI-based mobile robots: Case studies of successful robot systems. Cambridge, MA: MIT Press.
34. Kortenkamp, D., & Weymouth, T. (1994). Topological mapping for mobile robots using a combination of sonar and vision sensing. Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 979-984). Menlo Park, CA: AAAI Press/MIT Press.
35. Kuipers, B., & Byun, Y.-T. (1988). A robust qualitative method for spatial learning in unknown environments. Proceeding of Eighth National Conference on Artificial Intelligence (pp. 774-779). Menlo Park, CA: AAAI Press/The MIT Press.
36. Kuipers, B., & Byun, Y.-T. (1991). A robot exploration and mapping strategy based on a semantic hierachy of spatial representations. Journal of Robotics and Autonomous Systems, 8, 47-63.
37. Kuipers, B., & Levitt, T. (1988). Navigation and mapping in large-scale space. AI Magazine, 9, 25-43.
38. Leonard, J.J., & Durrant-Whyte, H.F. (1992). Directed Sonar Sensing for Mobile Robot Navigation. Boston, MA: Kluwer Academic Publishers.
39. Leonard, J., Durrant-Whyte, H., & Cox, I. (1992). Dynamic map building for an autonomous mobile robot. International Journal of Robotics Research, 11, 89-96.
40. Matarić, M.J. (1990). A distributed model for mobile robot environment-learning and navigation. Master's thesis, MIT, Artificial Intelligence Laboratory, Cambridge, MA.
41. Mine, H., & Osaki, S. (1970). Markovian Decision Processes. New York, NY: American Elsevier.
42. Moravec, H.P. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9, 61-74.
43. Moravec, H., & Martin, M. (1994). Robot navigation by 3D spatial evidence grids (Internal Report). Pittsburgh, PA: Carnegie Mellon University, The Robotics Institute.
44. Neven, H., & Schöner, G. (1996). Dynamics parametrically controlled by image correlations organize robot navigation. Biological Cybernetics, 75, 293-307.
45. Nourbakhsh, I., Powers, R., & Birchfield, S. (1995). DERVISH: An office-navigating robot. AI Magazine, 16, 53-60.
46. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann Publishers.
47. Peters, L., Surmann, H., Guo, S., Beck, K., & Huser, J. (1994). Moria fuzzy logik gesteuertes, autonomes fahrzeug (Internal Report). Sankt Augustin, Germany: German National Research Center for Information Technology (GMD).
48. Presson, C., & Montello, D. (1988). Points of reference in spatial cognition: Stalking the elusive landmark. British Journal of Developmental Psychology, 6, 378-381.
49. Rabiner, L.R. (1989). A tutorial on hidden Markov models and selected application in speech recognition. Proceedings of the IEEE. IEEE Log Number 8825949.
50. Rencken, W. (1993). Concurrent localisation and map building for mobile robots using ultrasonic sensors. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2129-2197). Yokohama, Japan.
51. Rencken, W. (1995). Autonomous sonar navigation in indoor, unknown and unstructired environments. In V. Graefe (Ed.), Intelligent robots and systems. Amsterdam: Elsevier.
52. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. rumelhart & J.L. McClelland (Eds.), Parallel distributed processing. Cambridge, MA: MIT Press.
53. Schiele, B., & Crowley, J. (1994). A comparison of position estimation techniques using occupancy grids. Proceedings of the 1994 IEEE International Conference on Robotics and Automation (pp. 1628-1634). San Diego, CA.
54. Simmons, R., & Koenig, S. (1995). Probabilistic robot navigation in partially observable environments. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 1080-1087). San Mateo, CA: Morgan Kaufmann.
55. Smith, R.C., & Cheeseman, P. (1985). On the representation and estimation of spatial uncertainty (Technical Report TR 4760 & 7239). Menlo Park, CA: SRI International.
56. Smith, R., Self, M., & Cheeseman, P. (1990). Estimating uncertain spatial relationships in robotics. In I. Cox & G. Wilfong (Eds.), Autonomous Robot Vehnicles. Berlin, Germany: Springer-Verlag.
57. Stanfill, C., & Waltz, D. (1986). Towards memory-based reasoning. Communications of the ACM, 29, 1213-1228.
58. Thrun, S. (1993). Exploration and model building in mobile robot domains. Proceedings of the International Conference on Neural Networks (pp. 175-180). San Francisco, CA: IEEE Neural Network Council.
59. Thrun, S. (1996). A Bayesian approach to landmark discovery and active perception for mobile robot navigation (Technical Report CMU-CS-96-122). Pittsburgh, PA: Carnegie Mellon University, Department of Computer Science.
60. Thrun, S. (1997). To know or not to know: On the utility of models in mobile robotics. AI Magazine, 18, 47-54.
61. Thrun, S. (in press). Learning maps for indoor mobile robot navigation. Artificial Intelligence.
62. Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig, D., Hofmann, T., Krell, M. & Schimdt, T. (in press). Map learning and high-speed navigation in RHINO. In D. Kortenkamp, R. Bonasso, & R. Murphy (Eds.), AI-based mobile robots: Case studies of successful robot systems. Cambridge, MA: MIT Press.
63. Thrun, S., Fox, D., & Burgard, W. (in press). A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots. Machine Learning and Autonomous Robots (joint issue).
64. Vapnik, V. (1982). Estimations of dependences based on statistical data. Berlin, Germany: Springer Publisher.
65. Wasserman, P.D. (1989). Neural computing: Theory and practice. Hew York, NY: Von Nostrand Reinhold.
66. Weiß, G., Wetzler, C., & von Puttkamer, E. (1994). Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. Proceedings of the International Conference on Intelligent Robots and Systems (pp. 595-601).
67. Wolfart, E., Fisher, R., & Walker, A. (1995). Position refinement for a navigating robot using motion information based on honey bee strategies. Proceedings of the International Symposium on Robotic Systems (SIR 95). Pisa, Italy.
68. Yamauchi, B., &., Beer, R. (1996). Spatial learning for navigation in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Special Issue on Learning Autonomous Robots. Also available at http://www.aic.nrl.navy.mil/~yamauchi/.
69. Yamauchi, B., & Langley, P. (1997). Place recognition in dynamic environments. Journal of Robotic Systems, Special Issue on Mobile Robots, 14, 107-120.