Abstract
This paper sketches and discusses design options for complex probabilistic state estimators and investigates their interactions and their impact on performance. We consider, as an example, the estimation of game states in autonomous robot soccer. We show that many factors other than the choice of algorithms determine the performance of the estimation systems. We propose empirical investigations and learning as necessary tools for the development of successful state estimation systems.
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© 2004 Springer-Verlag Berlin Heidelberg
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Schmitt, T., Hanek, R., Beetz, M. (2004). Developing Comprehensive State Estimators for Robot Soccer. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds) RoboCup 2003: Robot Soccer World Cup VII. RoboCup 2003. Lecture Notes in Computer Science(), vol 3020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25940-4_35
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DOI: https://doi.org/10.1007/978-3-540-25940-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22443-3
Online ISBN: 978-3-540-25940-4
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