T-SLAM: Registering Topological and Geometric Maps for Robot Localization
This article reports on a map building method that integrates topological and geometric maps created independently using multiple sensors. The procedure is termed T-SLAM to emphasize the integration of Topological and local Geometric maps that are created using a SLAM algorithm. The topological and metric representations are created independently, being local metric maps associated with topological places and registered at the topological level. The T-SLAM approach is mathematically formulated and applied to the localization problem within the Intelligent Robotic Porter System (IRPS) project, which is aimed at deploying mobile robots in large environments (e.g. airports). Some preliminary experimental results demonstrate the validity of the proposed method.
KeywordsKeywords Topological maps View-based localization SLAM geometric maps Robot localization
Unable to display preview. Download preview PDF.
- 1.B. J. Kuipers. The Spatial Semantic Hierarchy. Artificial Intelligence, Elsevier, 119:191–233, 2000. urlhttp://www.cs.utexas.edu/users/qr/papers/Kuipers-aij-00.html.
- 2.B. Kuipers, J. Modayil, P. Beeson, M. MacMahon, and F. Savelli. Local metrical and global topological maps in the Hybrid Spatial Semantic Hierarchy. In IEEE International Conference on Robotics and Automation (ICRA 2004), 2004.Google Scholar
- 4.I. Posner, D. Schroeter, and P. Newman. Using scene similarity for place labelling. In 10th International Symposium on Experimental Robotics, ISER 2006, 2006.Google Scholar
- 5.J. Folkesson and H. Christensen. Graphical slam - a self-correcting map. In IEEE International Conference on Robotics and Automation, 2004, 2004.Google Scholar
- 6.B. Lisien, D. Morales, D. Silver, G. Kantor, I. Rekleitis, and H. Choset. Hierarchical simultaneous localization and mapping. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robotic Systems, IROS, pages 448–453, las Vegas, Nevada, October 2003.Google Scholar
- 8.P. Newman, D. Cole, and K. Ho. Outdoor slam using visual appearance and laser ranging. In IEEE International Conference on Robotics and Automation, 2006, 2006.Google Scholar
- 9.M. Pfingsthorn, B. Slamet, and A. Visser. A scalable hybrid multi-robot slam method for highly detailed maps. In Proceedings of the 11th RoboCup International Symposium, 2007.Google Scholar
- 11.J. Gasóos and A. Saffiotti. Integrating fuzzy geometric maps and topological maps for robot navigation. In Proceedings of 3rd International ICSC Symposium on Soft Computing (SOCO’99), pages 754–760, Genova, Italy, 1999.Google Scholar
- 14.S. Thrun. Robotic mapping: A survey. In G. Lakemeyer and B. Nebel, editors, Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, 2002.Google Scholar
- 15.A. Eliazar and R. Parr. Dp-slam: Fast, robust simultaneous localization and mapping without predetermined landmarks. In Proceedings 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pages 1135–1142, 2003.Google Scholar
- 16.C. Stachniss, D. Haehnel, W. Burgard, and G. Grisetti. On actively closing loops in grid-based fastslam. The International Journal of the Robotics Society of Japan (RSJ), 19(10):1059–1080, 2005.Google Scholar
- 18.B. Limketkai, L. Liao, and D. Fox. Relational object maps for mobile robots. In International Joint Conference on Artificial Intelligence (IJCAI), 2005.Google Scholar
- 19.U. R. Zimmer. Embedding local metrical map patches in a globally consistent topological map. In Symposium on Underwater Technology 2000, Tokyo, Japan, May 23-26 2000, May 2000.Google Scholar
- 21.R. Thomas and S. Donikian. A model of hierarchical cognitive map and human memory designed for reactive and planned navigation. Technical report, l’Institut National de Recherche en Informatique et en Automatique, INRIA, Project siames, 2000.Google Scholar
- 22.D. Haehnel, W. Burgard, D. Fox, K. Fishkin, and M. Philipose. Mapping and localization with rfid technology. In IEEE International Conference on Robotics and Automation (ICRA), 2004, 2004.Google Scholar
- 24.K. Rohanimanesh, G. Theocharous, and S. Mahadevan. Hierarchical map learning for robot navigation. In In AIPS Workshop on Decision-Theoretic Planning, 2000.Google Scholar
- 25.G. Theocharous, K. Murphy, and L. P. Kaelbling. Representing hierarchical pomdps as dbns for multi-scale robot localization. In IEEE International Conference on Robotics and Automation, 2004, 2004.Google Scholar
- 26.F. Ferreira, V. Santos, and J. Dias. A topological path layout for autonomous navigation of multi-sensor robots. International Journal of Factory Automation, Robotics and Soft Computing, 1:203–215, 2007.Google Scholar
- 27.M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. Fast SLAM: A factored solution to the simultaneous localization and mapping problem. In Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, Canada, 2002. AAAI.Google Scholar
- 29.David G. Lowe. Distinctive Image Features-From Scale-Invariant Keypoints. IJCV, 60(2): 91–119, 2004.Google Scholar