| Autor: |
Vorst, Philipp, Zell, Andreas |
| Zdroj: |
European Robotics Symposium 2008; 2008, p273-282, 10p |
| Abstrakt: |
In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. RFID and position data, recorded during an exploration phase, are used to learn the probability of detecting an RFID tag, for which we investigate two non-parametric probability density estimation techniques. The trained model is finally used to localize the robot via a particle filter-based approach and optimized with respect to the resulting localization error. Experiments have shown that the learned models perform comparably well as a grid-based model learned from measurements in a stationary setup, but can be obtained easier. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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