Skip to Main content Skip to Navigation
Reports

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 SHARP - Automatic Programming and Decisional Systems in Robotics
GRAVIR - IMAG - Graphisme, Vision et Robotique, 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 new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automaticall- y 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.
Complete list of metadatas

Cited literature [1 references]  Display  Hide  Download

https://hal.inria.fr/inria-00071926
Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 7:16:59 PM
Last modification on : Wednesday, April 11, 2018 - 1:51:33 AM
Document(s) archivé(s) le : Sunday, April 4, 2010 - 10:44:55 PM

Identifiers

  • HAL Id : inria-00071926, version 1

Collections

Citation

Olivier Aycard, Jean-François Mari, Richard Washington. Learning to automatically detect features for mobile robots using second-order Hidden Markov Models. [Research Report] RR-4659, INRIA. 2002, pp.32. ⟨inria-00071926⟩

Share

Metrics

Record views

341

Files downloads

259