Name
Title | ||
|---|---|---|
Learning to automatically detect features for mobile robots using second-order Hidden Markov Models |
Abstract | ||
|---|---|---|
In this paper, we propose a new method based on Hidden Markov Models to inter- pret temporal sequences of sensor data from mo- bile robots to automatically detect features. Hid- den 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 abil- ity to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal se- quences 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 en- vironment where a different mobile robot has to identify situations like climbing a hill or crossing a rock. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.5772/5816 | international joint conference on artificial intelligence |
Keywords | Field | DocType |
sensor data interpretation,mobile robots,hidden markov models,hmm | Computer vision,Maximum-entropy Markov model,Pattern recognition,Computer science,Artificial intelligence,Hidden Markov model,Artificial neural network,Climbing,Mobile robot,Doors | Journal |
Volume | Citations | PageRank |
abs/cs/050 | 7 | 0.51 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olivier Aycard | 1 | 309 | 26.57 |
| Jean-francois Mari | 2 | 41 | 4.02 |
| Richard Washington | 3 | 54 | 4.04 |