Publication
Robot localization from minimalist inertial data using a Hidden Markov Model
| dc.contributor.author | Abreu, António | |
| dc.date.accessioned | 2014-07-22T11:38:06Z | |
| dc.date.available | 2014-07-22T11:38:06Z | |
| dc.date.issued | 2014-05-14 | |
| dc.description.abstract | Hidden Markov Models (HMM) are applied to interoceptive data (in this case the sense of rotation by way of a gyroscope) acquired by a moving wheeled robot when contouring an indoor environment. We demonstrate the soundness of HMM to solve the problem of robot localization in a topological model of the environment, particularly the kidnapped robot problem and position tracking. In this approach, the environment topology is described by the sequence of movements a robot executes when contouring the environment. Movements are described in a fuzzy domain using distance traveled and curvature as features. | por |
| dc.description.sponsorship | Com o apoio RAADRI. | por |
| dc.identifier.uri | http://hdl.handle.net/10400.26/6593 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | IEEE | por |
| dc.relation.publisherversion | DOI: 10.1109/ICARSC.2014.6849794 | por |
| dc.subject | Robot localization | por |
| dc.subject | Markov Models | por |
| dc.title | Robot localization from minimalist inertial data using a Hidden Markov Model | por |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Espinho, Portugal | por |
| oaire.citation.endPage | 252 | por |
| oaire.citation.startPage | 247 | por |
| oaire.citation.title | ICARSC/Robótica 2014 | por |
| rcaap.rights | openAccess | por |
| rcaap.type | conferenceObject | por |