Robot localization from minimalist inertial data using a Hidden Markov Model
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 t...
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| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2014
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| Texto completo: | http://hdl.handle.net/10400.26/6593 |
| País: | Portugal |
| Oai: | oai:comum.rcaap.pt:10400.26/6593 |
| Resumo: | 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. |
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