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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ozkucur, N.E. Akin, H.L. |
| Copyright Year | 2013 |
| Description | Author affiliation: Bilgisayar Muhendisligi Bolumu, Bogazici Univ., İstanbul, Turkey (Ozkucur, N.E.; Akin, H.L.) |
| Abstract | Most of the mapping methods in indoor environments use single type of feature. Typically an expert decides which feature type to be used. In this work, the robot selects suitable feature type based on the current place during mapping. During the training phase, an expert labels places with the suitable feature type. The robot calculates a metric for each feature type and trains a hidden Markov model with the labels provided by the expert. In a new environment, the robot uses the trained hidden Markov model and selects suitable feature type. Hierarchical topological mapping enables usage of different feature types in the map representation. This method increases the adaptability of the mapping system in new environments and enables different feature types to be utilized in the same environment. The devised method is applied to a robot equipped with a laser sensor and a camera in a simulation environment and tested in an indoor environment. |
| Sponsorship | IEEE Turkey Sect. SP Chapter |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 587647 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467355629 |
| e-ISBN | 9781467355636 |
| e-ISBN | 9781467355612 |
| DOI | 10.1109/SIU.2013.6531556 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-04-24 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Supervised Learning Automation Conferences Semantics Hidden Markov models Markov processes Robot sensing systems Mapping Hidden Markov Model Robotics |
| Content Type | Text |
| Resource Type | Article |
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