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Ramos, F. T., Upcroft, B., Kumar, S. and Durrant-Whyte, H. F. (2005). A Bayesian Approach for Place Recognition. In: IJCAI-05 Workshop: Reasoning with Uncertainty in Robotics (RUR-05), Edinburgh, Scotland, (1-8). 30 Jul 2005. 290  
Wang, X., Kumar, S., Kaupp, T., Upcroft, B. and Durrant-Whyte, H. F. (2005). Applying ISOMAP to the learning of hyperspectral image. In: Claude Sammut, Proceedings of the 2005 Australasian Conference on Robotics & Automation. Australasian Conference on Robotics and Automation 2005, Sydney, Austalia, (). 5 - 7 Dec 2005. 99  
Kumar, S., Ramos, F., Upcroft, B. and Durrant-Whyte. H. (2005). A statistical framework for natural feature representation. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems 2005 (IROS 2005), Edmonton, Canada, (1582-1587). 2-6 August 2005. doi:10.1109/IROS.2005.1544950 61   0 Cited 1 times in Scopus1 0
Kumar, S., Ramos, F., Upcroft, B., Ridley, M., Ong, L., Sakkarieh, S. and Durrant-Whyte, H. (2005). A stochastic model for natural feature representation. In: 2005 7th International Conference on Information Fusion (FUSION). 7th International Conference on Information Fusion, Philadelphia, U.S., (1030-1037). 25-28 July, 2005. doi:10.1109/ICIF.2005.1591971 50   Cited 1 times in Scopus1 0
Douillard, B., Upcroft, B., Kaupp, T., Ramos, F. and Durrant-Whyte, H. (2007). Bayesian filtering over compressed appearance states. In: Matthew Dunbabin and Mandyam Srinivasan, Proceedings of the Australasian Conference on Robotics and Automation 2007. Australasian Conference on Robotics and Automation 2007 - ACRA 2007, Brisbane, Australia, (). 10-12 December, 2007. 29  
Ong, L-L., Bailey, T., Durrant-Whyte, H. and Upcroft, B. (2008). Decentralised particle filtering for multiple target tracking in wireless sensor networks. In: information Fusion 2008. 11th International Conference on Information Fusion, United States, (342-349). 30 June - 3 July 2008. doi:10.1109/ICIF.2008.4632230 67   Cited 9 times in Scopus9 0
Ramos, F. T., Durrant-Whyte, H. F., Upcroft, B. and Kumar, S. (2006). Learning articulated motion structures with Bayesian Networks. In: IEEE. 8th International Conference on Information Fusion, Franklin Plaza, Philadelphia, USA., (). 25-28 July, 2005. doi:10.1109/ICIF.2005.1591927 32   0
Sprinkle, J, Eklund, JM, Gonzalez, H, Grotli, EI, Upcroft, B, Makarenko, A, Uther, W, Moser, M, Fitch, R, Durrant-Whyte, H and Sastry, SS (2009) Model-based design: a report from the trenches of the DARPA Urban Challenge. SOFTWARE AND SYSTEMS MODELING, 8 4: 551-566. doi:10.1007/s10270-009-0116-5 60   1 Cited 11 times in Scopus11 0
Upcroft, B., Ridley, M., Ong, L. L., Douillard, B., Kaupp, T., Kumar, S., Bailey, T., Ramos, F., Makarenko, A., Brooks, A., Sukkarieh, S. and Durrant-Whyte, H. F. (2008). Multi-level state estimation in an outdoor decentralised sensor network. In: Oussama Khatib, Vijay Kumar and Daniela Rus, Springer Tracts in Advanced Robotics. Experimental Robotics: The 10th International Symposium on Experimental Robotics. ISER '06: 10th International Symposium on Experimental Robotics 2006, Rio de Janeiro, Brazil, (355-365). 6-10 July, 2006. doi:10.1007/978-3-540-77457-0_33 59   Cited 2 times in Scopus2 0
Ramos, F.T., Kumar, S., Upcroft, B. and Durrant-Whyte, H. F. (2005). Representing natural objects in unstructured environments. In: 19th Annual Conference on Neural Information Processing Systems: NIPS Workshop on Machine Learning in Robotics, Whistler, Canada, (1-11). 9-10 December, 2005. 34  
Upcroft, B., Ong, L L., Kumar, S., Ridley, M., Bailey, T., Sukkarieh, S. and Durrant-Whyte, H. (2005). Rich probabilistic representations for bearing only decentralised data fusion. In: 2005 7th International Conference on Information Fusion (FUSION. 7th International Conference on Information Fusion (FUSION), Philadelphia, PA, USA, (1054-1061). 25 - 28 Jul 2005. doi:10.1109/ICIF.2005.1591974 60   0 Cited 6 times in Scopus6 0
Kumar, S., Guivant, B., Upcroft, H. F. and Durrant-Whyte, H. F. (2007) Sequential nonlinear manifold learning. Intelligent Data Analysis, 11 2: 203-222. 53   Cited 1 times in Scopus1