Abstract
Kalman Filters (KF) are at the root of many computational solutions for autonomous systems navigation problems, besides other application domains. The basic linear formulation has been extended in several ways to cope with non-linar dynamic environments. One of the latest trend is to introduce other Computational Intelligence (CI) tools, such as Fuzzy Systems or Artificial Neural Networks inside its computational loop, in order to obtain learning and advanced adaptive properties. This paper offers a short review of current approaches.
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References
Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (slam): part ii. Robotics & Automation Magazine 13(3), 108–117 (2006)
Carrasco, R., Cipriano, A.: Fuzzy logic based nonlinear kalman filter applied to mobile robots modelling. In: 2004 IEEE international conference on fuzzy systems, vol. 3, pp. 25–29 (2004)
Chatterjee, A., Matsuno, F.: A neuro-fuzzy assisted extended kalman filter-based approach for simultaneous localization and mapping (slam) problems. IEEE Transactions on Fuzzy Systems 15(5), 984–997 (2007)
Choi, M., Sakthivel, R., Chung, W.K.: Neural network-aided extended kalman filter for slam problem. In: IEEE International Conference on Robotics and Automation, pp. 1686–1690 (2007)
Di Marco, M., Garulli, A., Giannitrapani, A., Vicino, A.: Simultaneous localization and map building for a team of cooperating robots: a set membership approach. IEEE Transactions on Robotics and Automation 19(2), 238–249 (2003)
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. Robotics & Automation Magazine 13(2), 99–110 (2006)
Franz, M.O., Mallot, H.A.: Biomimetic robot navigation. Robotics and Autonomous Systems 30(1-2), 133–153 (2000)
Julier, S.J., Uhlmann, J.K.: A new extension of the kalman filter to nonlinear systems. In: Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls (1997)
Jwo, D.-J., Wang, S.-H.: Adaptive fuzzy strong tracking extended kalman filtering for gps navigation. Sensors Journal 7(5), 778–789 (2007)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Transaction of the ASME - Journal of Basic Engineering, 35–45 (1960)
Mandel, J.: A brief tutorial on the ensemble kalman filter. Technical Report CCM Report 242, University of Colorado at Denver and Health Sciences Center (February 2007)
Martin, F., Matellan, V., Barrera, P., Cañas, J.M.: Localization of legged robots combining a fuzzy-markov method and a population of extended kalman filters. Robotics and Autonomous Systems 55(12), 870–880 (2007)
Meng, Q., Lee, M.: Error-driven active learning in growing radial basis function networks for early robot learning. Neurocomputing 71(7-9), 1449–1461 (2008)
Murphy, K., Russell, S.: Rao-Blackwellised particle filtering for dynamic bayesian networks. In: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Najjaran, H., Goldenberg, A.: Real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive kalman filter. Robotics and Autonomous Systems 55(2), 96–106 (2007)
Pathiranage, C.D., Watanabe, K., Izumi, K.: Simultaneous localization and mapping (slam) based on pseudolinear measurement model with a bias reduction approach. In: International Conference on Industrial and Information Systems. ICIIS 2007, pp. 73–78 (2007)
Reina, G., Vargas, A., Nagatani, K., Yoshida, K.: Adaptive kalman filtering for GPS-based mobile robot localization. In: IEEE International Workshop on Safety, Security and Rescue Robotics. SSRR 2007, pp. 1–6 (2007)
Simon, D.: Training fuzzy systems with the extended kalman filter. Fuzzy Sets and Systems 132(2), 189–199 (2002)
Simon, D.: Training radial basis neural networks with the extended kalman filter. Neurocomputing 48, 455–475 (2002)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Autonomous Robot Vehicles, pp. 167–193. Springer, Heidelberg (1990)
Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Technical Report TR 4760 & 7239, SRI (1985)
Thrun, S.: Robotic Mapping: A Survey. In: Exploring Artificial Intelligence in the New Millenium (2002)
Welch, G., Bishop, G.: An introduction to the kalman filter. In: SIGGRAPH 2001 In Computer Graphics, Annual Conference on Computer Graphics & Interactive Techniques. ACM Press, New York (2001)
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Graña, M., Villaverde, I., Guede, J.M.L., Fernández, B. (2009). Review of Hybridizations of Kalman Filters with Fuzzy and Neural Computing for Mobile Robot Navigation. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_15
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DOI: https://doi.org/10.1007/978-3-642-02319-4_15
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