Abstract
In this section, we describe a method based on hidden Markov models for learning and recognizing places in an indoor environment by a mobile robot. Hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (e.g. neural networks, ... ) are their capabilities to modelize noisy temporal signals of variable length.
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References
Laroche, P., Charpillet, J., and Schott, R. (1999). Mobile Robotics Planning Using Abstract Markov Decision Processes. pages 299–306. ICTAI’99, IEEE.
Aycard, O., Charpillet, F., Fohr, D., and Mari, J.-F. (1997a). Place Learning and Recognition Using Hidden Markov Models. In Proceedings IEEE-RSJ on International Conference on Intelligent Robots and Systems, pages 1741 — 1746, Grenoble, France.
Aycard, 0., Mari, J.-F., and Charpillet, F. (1998). Second Order Hidden Markov Models for Places Recognition: New Results. In proceedings of IEEE International Conference on Tools with Artificial Intelligence.
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© 2001 Springer Science+Business Media New York
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Mari, JF., Schott, R. (2001). Some Applications in Robotics. In: Probabilistic and Statistical Methods in Computer Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6280-8_5
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DOI: https://doi.org/10.1007/978-1-4757-6280-8_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4877-9
Online ISBN: 978-1-4757-6280-8
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