Data driven MCMC for appearance-based topological mapping

Robotics: Science and Systems

Volumn 1, Issue , 2005, Pages 209-216

  Ranganathan, Ananth ^a   Dellaert, Frank ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL ROBOTS; MAPPING; MARKOV CHAINS; ROBOTICS; TOPOLOGY;

MARKOV CHAIN MONTE CARLO; POSTERIOR DISTRIBUTIONS; PROBABILISTIC FRAMEWORK; PROBABILISTIC TECHNIQUE; PROBABILISTIC TOPOLOGICAL MAPS; PROPOSAL DISTRIBUTION; SENSOR MEASUREMENTS; TOPOLOGICAL MAPPING;

PROBABILITY DISTRIBUTIONS;

EID: 77957148776     PISSN: None     EISSN: 2330765X     Source Type: Conference Proceeding    
DOI: 10.15607/rss.2005.i.028     Document Type: Conference Paper

References (21)

1. H. Choset and K. Nagatani. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Trans. Robot. Automat., 17(2):125-137, April 2001.
2. G. Dudek and D. Jugessur. Robust place recognition using local appearance based methods. In IEEE Intl. Conf. on Robotics and Automation (ICRA), pages 1030-1035, 2000.
3. A. Elfes. Occupancy grids: A probabilistic framework for robot perception and navigation. Journal of Robotics and Automation, RA-3(3):249-265, June 1987.
4. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, editors. Markov chain Monte Carlo in practice. Chapman and Hall, 1996.
5. D. Haehnel, W. Burgard, D. Fox, and S. Thrun. A highly effi cient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2003.
6. H. Ishiguro, K. C. Ng, R. Capella, and M. M. Trivedi. Omnidirectional image-based modeling: three approaches to approximated plenoptic representations. Machine Vision and Applications, 14(2):94-102, 2003.
7. S. Jain and R. Neal. A split-merge Markov chain Monte Carlo procedure for the dirichlet process mixture model. Journal of Computational and Graphical Statistics, 13(1):158-182, March 2004.
8. I. T. Jolliffe. Principal Component Analysis. Springer, 1986.
9. Benjamin Kuipers and Patrick Beeson. Bootstrap learning for place recognition. In AAAI Nat. Conf. on Artificial Intelligence, pages 174-180, 2002.
10. B.J. Kuipers and Y.-T. Byun. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems, 8:47-63, 1991.
11. E. Menegatti, M. Zoccarato, E. Pagello, and H. Ishiguro. Image-based Monte Carlo localisation with omnidirectional images. Robotics and Autonomous Systems, 48(1):17-30, 2004.
12. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In AAAI Nat. Conf. on Artificial Intelligence, 2002.
13. A. Nijenhuis and H. Wilf. Combinatorial Algorithms. Academic Press, 2 edition, 1978.
14. A. Ranganathan and F. Dellaert. Inference in the space of topological maps: An MCMC-based approach. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2004.
15. E. Remolina and B. Kuipers. Towards a general theory of topological maps. Artificial Intelligence, 152(1):47-104, 2004.
16. F. Savelli and B. Kuipers. Loop-closing and planarity in topological map-building. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2004.
17. H. Shatkay and L. Kaelbling. Learning topological maps with weak local odometric information. In Proceedings of IJCAI-97, pages 920-929, 1997.
18. B. Stewart, J. Ko, D. Fox, and K. Konolige. The revisiting problem in mobile robot map building: A hierarchical Bayesian approach. In Conf. on Uncertainty in Artificial Intelligence, pages 551-558, 2003.
19. K. Sugihara. Some location problems for robot navigation using a single camera. CVGIP:Image Understanding, 42(1):112-129, April 1988.
20. N. Tomatis, I. Nourbakhsh, and R. Siegwart. Hybrid simultaneous localization and map building: Closing the loop with multi-hypotheses tracking. In IEEE Intl. Conf. on Robotics and Automation (ICRA), pages 2749-2754, 2002.
21. Z.W. Tu and S.C. Zhu. Image segmentation by data-driven Markov chain Monte Carlo. IEEE Trans. Pattern Anal. Machine Intell., 24(5):657-673, 2002.