Name
Abstract | ||
|---|---|---|
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 (e.g. 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 five distinctive places are given |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/IROS.1997.656595 | Intelligent Robots and Systems, 1997. IROS '97., Proceedings of the 1997 IEEE/RSJ International Conference |
Keywords | Field | DocType |
hidden Markov models,learning (artificial intelligence),mobile robots,object recognition,path planning,hidden Markov models,indoor environment,infrared sensors,mobile robot,noisy temporal signals,object recognition,place learning,tactile sensors,ultrasonic sensors | Motion planning,Computer vision,Signature recognition,Computer science,Stochastic process,Artificial intelligence,Robot,Hidden Markov model,Artificial neural network,Machine learning,Mobile robot,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISBN | Citations |
3 | 0-7803-4119-8 | 19 |
PageRank | References | Authors |
2.15 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Aycard | 1 | 309 | 26.57 |
| Francois Charpillet | 2 | 154 | 16.96 |
| dominique fohr | 3 | 239 | 49.61 |
| Jean-francois Mari | 4 | 41 | 4.02 |