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Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Olivier Aycard 1 Jean-François Mari 2 Richard Washington 3 
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
2 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we propose a robust method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.
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Submitted on : Tuesday, September 26, 2006 - 9:41:26 AM
Last modification on : Friday, November 18, 2022 - 9:26:00 AM


  • HAL Id : inria-00099809, version 1



Olivier Aycard, Jean-François Mari, Richard Washington. Learning to automatically detect features for mobile robots using second-order Hidden Markov Models. proceedings of the IJCAI'03 - Workshop on Reasoning with Uncertainty in Robotics - IJCAI-RUR'03, Aug 2003, Acapulco, Mexico, France. 8 p. ⟨inria-00099809⟩



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