Abstract
In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the non-linear parts of the state estimation problem. In the context of target tracking, these are the non-linear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a four-legged AIBO robot and tested in the context of ball tracking in the RoboCup domain.
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References
Bar-Shalom, Y., Li, X.-R.: Multitarget-Multisensor Tracking: Principles and Techniques. Yaakov Bar-Shalom (1995)
Bar-Shalom, Y., Li, X.-R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. John Wiley, Chichester (2001)
Doucet, A., de Freitas, J.F.G., Murphy, K., Russell, S.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: Proc. of the Conference on Uncertainty in Artificial Intelligence (2000)
Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo in Practice. Springer, NewYork (2001)
Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing 49(3) (2001)
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.-J.: Particle filters for positioning, navigation and tracking. IEEE Transactions on Signal Processing 50(2) (2002)
Kwok, C.T., Fox, D., Meilă, M.: Adaptive real-time particle filters for robot localization. In: Proceedings of the 2003 IEEE International Conference on Robotics Automation (ICRA 2003), Taipei, Taiwan (September 2003)
Kwok, C.T., Fox, D., Meilă, M.: Real-time particle filters. IEEE Special Issue on Sequential State Estimation (March 2004)
Montemerlo, M., Thrun, S., Whittaker, W.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proc. of the IEEE International Conference on Robotics & Automation (2002)
Schmitt, T., Hanek, R., Beetz, M., Buck, S., Radig, B.: Cooperative probabilistic state estimation for vision-based autonomous mobile robots. IEEE Transactions on Robotics and Automation 18(5) (2002)
Schulz, D., Burgard, W., Fox, D.: People tracking with mobile robots using sample-based joint probabilistic data association filters. International Journal of Robotics Research 22(2) (2003)
Wan, E.A., van der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proc. of Symposium 2000 on Adaptive Systems for Signal Processing, Communications, and Control (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Kwok, C., Fox, D. (2005). Map-Based Multiple Model Tracking of a Moving Object. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds) RoboCup 2004: Robot Soccer World Cup VIII. RoboCup 2004. Lecture Notes in Computer Science(), vol 3276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32256-6_2
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DOI: https://doi.org/10.1007/978-3-540-32256-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25046-3
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