Paper Info
Title
Second order hidden Markov models for place recognition: new results
Abstract
Second order hidden Markov models have been used for a long time in pattern recognition, especially in speech reco g- nition. Their main advantages over other methods (neural networks . . . ) are their capabilities to model noisy tempo- ral signals of variable length. In a previous work, we pro- posed a new method based on second order hidden Markov models to learn and recognize places in an indoor environ- ment by a mobile robot, and showed that this approach is well suited for learning and recognizing places. In this pa- per, we propose major modifications to increase the global rate of places recognition. Results of experiments on a real robot with distinctive places are given.
Year
DOI
Venue
1998
10.1109/TAI.1998.744879
Taipei
Keywords
Field
DocType
hidden Markov models,mobile robots,pattern classification,pattern recognition,global place recognition rate,indoor environment,mobile robot,noisy temporal signal modeling,pattern recognition,place learning,place recognition,second order hidden Markov models
Signature recognition,Computer science,Speech recognition,Feature (machine learning),Artificial intelligence,Robot,Hidden Markov model,Artificial neural network,Machine learning,Mobile robot
Conference
ISSN
ISBN
Citations 
1082-3409
0-7803-5214-9
5
PageRank 
References 
Authors
0.49
8
3
Name
Order
Citations
PageRank
Olivier Aycard130926.57
Jean-francois Mari2414.02
Francois Charpillet315416.96