Field and Service Robotics pp 143-154 | Cite as
Outdoor Simultaneous Localisation and Mapping Using RatSLAM
Summary
In this paper an existing method for indoor Simultaneous Localisation and Mapping (SLAM) is extended to operate in large outdoor environments using an omnidirectional camera as its principal external sensor. The method, RatSLAM, is based upon computational models of the area in the rat brain that maintains the rodent’s idea of its position in the world. The system uses the visual appearance of different locations to build hybrid spatial-topological maps of places it has experienced that facilitate relocalisation and path planning. A large dataset was acquired from a dynamic campus environment and used to verify the system’s ability to construct representations of the world and simultaneously use these representations to maintain localisation.
Keywords
SLAM Omnidirectional VisionPreview
Unable to display preview. Download preview PDF.
References
- 1.A. Arleo, F. Smeraldi, S. Hug, and W. Gerstner. Place cells and spatial navigation based on vision, path integration, and reinforcement learning. Advances in Neural Information Processing Systems, 2001.Google Scholar
- 2.G. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, and M. Csorba. A solution to the simultaneous localization and map building (SLAM) problem. Robotics and Automation, IEEE Transactions on, 17(3):229–241, 2001.CrossRefGoogle Scholar
- 3.M. O. Franz, B. Schölkopf, H. A. Mallot, and H. H. Bülthoff. Learning view graphs for robot navigation. Autonomous Robots, 5(1):111–125, 1998.MATHCrossRefGoogle Scholar
- 4.J. Košecká and F. Li. Vision based topological markov localization. In Proceedings International Conference on Robotics and Automation, volume 2, pages 1481–1486, 2004.Google Scholar
- 5.P. Lamon, A. Tapus, E. Glauser, N. Tomatis, and R. Siegwart. Environmental modeling with fingerprint sequences for topological global localization. In Proceedings of the International Conference on Intelligent Robots and Systems, volume 4, pages 3781–3786, 2003.Google Scholar
- 6.M. Milford, G. Wyeth, and D. Prasser. RatSLAM: a hippocampal model for simultaneous localization and mapping. In Proceedings of the International Conference on Robotics and Automation, volume 1, pages 403–408, 2004.Google Scholar
- 7.J. Nieto, J. Guivant, E. Nebot, and S. Thrun. Real time data association for FastSLAM. In Proceedings International Conference on Robotics and Automation, 2003.Google Scholar
- 8.D. Prasser, G. Wyeth, M. Milford, J. Roberts, and K. Usher. Experiments in outdoor operation of RatSLAM. In Proceedings of the 2004 Australasian Conference on Robotics and Automation. Canberra, 2004.Google Scholar
- 9.P.E. Rybski, F. Zacharias, and J.-F. Lett. Using visual features to build topological maps of indoor environments. In Proceedings International Conference on Robotics and Automation, volume 1, pages 850–855, 2003.Google Scholar
- 10.I. Ulrich and I. Nourbakhsh. Appearance-based place recognition for topological localization. In Proceedings of the IEEE International Conference on Robotics and Automation, volume 2, pages 1023–1029, 2000.Google Scholar
- 11.K. Usher, P. Ridley, and P. Corke. Visual servoing of a car-like vehicle-an application of omnidirectional vision. In Proceedings of the IEEE International Conference on Robotics and Automation, volume 3, pages 4288–4293, 2003.Google Scholar
- 12.J. Wolf, W. Burgard, and H. Burkhardt. Using an image retrieval system for vision-based mobile robot localization. In Proc. of the International Conference on Image and Video Retrieval (CIVR). 2002.Google Scholar