Place learning and recognition using hidden Markov models
نویسندگان
چکیده
In this paper, we propose a new method based on Hidden Markov Models to learn and recognize 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 (neural networks. . .) are their capabilities to modelize noisy temporal signals of variable length. We show in this paper that this approach is well adapted for learning and recognition of places by a mobile robot. Results of experiments on a real robot with ve distinctive places are given.
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تاریخ انتشار 1997