Long-term learning using multiple models for outdoor autonomous robot navigation

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2007, Pages 3158-3165

  Procopio, Michael J ^a   Mulligan, Jane ^a   Grudic, Greg ^a  

^a UCB 450   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMOUS ROBOT NAVIGATION; INTERNATIONAL CONFERENCES; LONG-TERM LEARNING;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); EDUCATION; INTELLIGENT ROBOTS; INTELLIGENT SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NAVIGATION; NAVIGATION SYSTEMS;

ROBOTS;

EID: 51349116926     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2007.4399583     Document Type: Conference Paper

References (24)

1. R. Wallace, K. Matsuzaki, J. Crisman, Y. Goto, J. Webb, and T. Kanade, "Progress in robot road-following," in Proc. of 1986 IEEE International Conference on Robotics and Automation, vol. 3, 1986, pp. 1615-1621.
2. J. Crisman and C. Thorpe, "Unscarf, a color vision system for the detection of unstructured roads," in Proc. 1991 Int. Conf. on Robotics and Automation, Sacramento, CA, April 1991, pp. 2496-2501.
3. D. Kuan, G. Phipps, and A.-C. Hsueh, "Autonomous robotic vehicle road following," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 5, pp. 648 - 658, 1988.
4. M. Turk, D. Morgenthaler, K. Gremban, and M. Marra, "Vits-a vision system for autonomous land vehicle navigation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 3, May 1988.
5. G. N. DeSouza and A. C. Kak, "Vision for mobile robot navigation: A survey," IEEE Trans. Pattern Anal. Much. Intell., vol. 24, no. 2, pp. 237-267, 2002.
6. M. Happold, M. Ollis, and N. Johnson, "Enhancing supervised terrain classification with predictive unsupervised learning," in Proceedings of Robotics: Science and Systems, Cambridge, USA, June 2006.
7. G. Grudic and J. Mulligan, "Outdoor path labeling using polynomial mahalanobis distance," in Proceedings of Robotics: Science and Systems, Cambridge, USA, June 2006.
8. H. Dahlkamp, A. Kaehler, D. Stavens, S. Thrun, and G. Bradski, "Self-supervised monocular road detection in desert terrain," in Proceedings of Robotics: Science and Systems, Cambridge, USA, June 2006.
9. R. Manduchi, A. Castano, A. Talukder, and L. Matfhies, "Obstacle detection and terrain classification for autonomous off-road navigation," Auton. Robots, vol. 18, no. 1, pp. 81-102, 2005.
10. L. Jackel, E. Krotkov, M. Perschbacher, J. Pippine, and C. Sullivan, "The DARPA LAGR program: Goals, challenges, methodology, and Phase I results," Journal of Field Robotics, vol. 23, pp. 945-973, November/December 2006.
11. J-Sun, T. Mehta, D. Wooden, M. Powers, J. Regh, T. Balch, and M. Egerstedt, "Learning from examples in unstructured, outdoor environments," Journal of Field Robotics, vol. 23, pp. 1019-1036, November/December 2006.
12. A. Howard, M. Turmon, A. Angelova, L. Matthies, and B. Tang, "Learned traversability for robot navigation: From underfoot to the far held," Journal of Field Robotics, vol. 23, pp. 1005-1017, November/December 2006.
13. W. N. Street and Y. Kim, "A streaming ensemble algorithm (sea) for large-scale classification," in KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM Press, 2001, pp. 377-382.
14. K. Nishida, K. Yamauchi, and T. Omori, "Ace: Adaptive classifiers-ensemble system for concept-drifting environments." in Multiple Classifier Systems, 2005, pp. 176-185.
15. R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes, "Ensemble selection from libraries of models," in ICML '04: Proceedings of the twenty-first international conference on Machine learning. New York, NY, USA: ACM Press, 2004, p. 18.
16. M. J. Procopio, T. Strohmann, A. R. Bates, G. Grudic, and J. Mulligan, "Using binary classifiers to augment stereo vision for enhanced autonomous robot navigation," University of Colorado at Boulder, Boulder, CO, Tech. Rep. CU-CS-1027-07, April 2007.
17. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York: Springer, 2001.
18. J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schoelkopf, and D. Schuurmans, Eds., 2000, pp. 61-74.
19. G. Stockman and L. G. Shapiro, Computer Vision. Upper Saddle River, NJ, USA: Prentice Hall PTR, 2001.
20. H. Moravec and A. E. Elfes, "High resolution maps from wide angle sonar," in Proceedings of the 1985 IEEE International Conference on Robotics and Automation, March 1985, pp. 116 - 121.
21. T. Joachims, "Training linear SVMs in linear time," in KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM Press, 2006, pp. 217-226.
22. M. J. Procopio, "Hand-labeled DARPA LAGR datasets." [Online]. Available: http://ml.cs.colorado.edu/∼procopio/
23. C. C. Chang and C. J. Lin, LIBSVM: a library for support vector machines, 2001, software available at http://www.csie.ntu.edu.tw/∼cjlin/ libsvm/.
24. J. Hoeting, D. Madigan, A. Raftery, and C. T. Volinsky, "Bayesian Model Averaging: A tutorial (with discussion)," Statistical Science, vol. 14, no. 4, pp. 382-417, 1999.