Topological map learning from outdoor image sequences

Journal of Field Robotics

Volumn 23, Issue 11-12, 2006, Pages 1091-1104

  He, Xuming ^a   Zemel, Richard S ^a   Mnih, Volodymyr ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; FEATURE EXTRACTION; PATTERN MATCHING; TOPOLOGY;

IMAGES SEQUENCES; TOPOLOGICAL MAP;

IMAGE ANALYSIS;

EID: 33846985937     PISSN: 15564959     EISSN: 15564967     Source Type: Journal    
DOI: 10.1002/rob.20170     Document Type: Article

References (26)

1. Beis, J.S., & Lowe, D.G. (1997, June). Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), San Juan, Puerto Rico.
2. Bengio, Y., Paiement, J., Vincent, P., Delalleau, O., Le Roux, N., & Ouimet, M. (2004, December). Out-of-sample extensions for LLE, ISOMAP, MDS, eigenmaps, and spectral clustering. In Advances in Neural Information Processing Systems 16, Vancouver, BC.
3. Bowling, M., Wilkinson, D., Ghodsi, A., & Milstein, A. (2005, October). Subjective localization with action re-specting embedding. In International Symposium on Robotics Research, San Francisco, CA.
4. Bradley, D.M., Patel, R., Vandapel, N., & Thayer, S.M. (2005, August). Real-time image-based topological localization in large outdoor environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS05), Edmonton, Alberta.
5. Carneiro, G., & Jepson, A.D. (2005, June). The distinctiveness, detectability, and robustness of local image features. In IEEE Computer Society International Conference on Computer Vision and Pattern Recognition (CVPR05), San Diego, CA.
6. Davison, A.J. (2003, Octobor). Real-time simultaneous localisation and mapping with a single camera. In The Tenth International Conference on Computer Vision (ICCV05), Beijing, China.
7. Grudic, G., & Mulligan, J. (2005, June). Topological mapping with multiple visual manifolds. In Proceedings of Robotics: Science and Systems 2005, Cambridge, MA.
8. Ham, J., Lin, Y., & Lee, D.D. (2005, August). Learning nonlinear appearance manifolds for robot localization. In. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS05), Edmonton, Alberta.
9. Hinton, G., & Roweis, S. (2002, December). Stochastic neighbor embedding. In Advances in Neural Information Processing Systems 15, Vancouver, BC.
10. Kosecka, J., & Yang, X. (2004, May). Location recognition and global localization based on scale-invariant keypoints. In ECCV 2004 Workshop on Statistical Learning in Computer Vision, Prague, Czech.
11. Kuipers, B., & Beeson, P. (2002, July). Bootstrap learning for place recognition. In The Eighteenth National Conference on Artificial Intelligence (AAAI-02), Edmonton, Alberta.
12. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.
13. Mason, L., Baxter, J., Bartlett, R, & Frean, M. (2000). Functional gradient techniques for combining hypotheses. In Smola, A.J. (Ed.), Advances in large margin classifiers (pp. 221-246). Cambridge, MA: MIT Press.
14. Perkins, S., Lacker, K., & Theiler, J. (2003). Grafting: Fast, incremental feature selection by gradient descent in function space. Journal of Machine Learning Research, 3, 1333-1356.
15. Rahimi, A., Recht, B., & Darrell, T. (2005, June). Learning appearance manifolds from video. In IEEE Computer Society International Conference on Computer Vision and Pattern Recognition (CVPR05), San Diego, CA.
16. Robertson, D., & Cipolla, R. (2004, September). An image-based system for urban navigation. In Proceedings of the British Machine Vision Conference 2004 (BMVC04), London, UK.
17. Sala, P., Sim, R., Shokoufandeh, A., & Dickinson, S.J. (2004, September). Landmark selection for vision-based navigation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS04), Sendai, Japan.
18. Se, S., Lowe, D., & Little, J. (2002). Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research, 21(8), 735-758.
19. Se, S., Lowe, D., & Little, J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 21(3), 364-375.
20. Sim, S., & Dudek, G. (2001, December). Learning generative models of scene features. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR01), Kauai, Hawaii.
21. Sim, R., & Dudek, G. (2004, July). Self-organizing visual maps. In Proceedings of the National Conference on Artificial Intelligence (AAAI04), San Jose, CA.
22. Takeuchi, Y., & Hebert, M. (1998, June). Finding images of landmarks in video sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR98), Santa Barbara, CA.
23. Tenenbaum, J., de Silva, V., & Langford, J. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323.
24. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge, MA: MIT Press.
25. Ulrich, I., & Nourbakhsh, I. (2000, April). Appearance-based place recognition for topological localization. In Proceedings of the 2000 IEEE International Conference on Robotics and Automation (ICRA00), San Francisco, CA.
26. Wolf, J., Burgard, W., & Burkhardt, H. (2002, July). Using an image retrieval system for vision-based mobile robot localization. In Proceedings of the International Conference on Image and Video Retrieval (CIVR02), London, UK.