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Descripción
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| A primary task for any mobile robot is to localize itself accurately from a reference frame system. In order to accomplish this, most of the existing algorithms fuse odometry sensors with laser range finders or sonar sensors. Usually, a common algorithm for data fusion is provided by using an Extended Kalman Filter. Nevertheless, an important drawback of this approach is the common lack of knowledge of odometry and kinematic model error statistics. Typically, the first and second statistic moment of these error models are not only unknown, but also might be time variant. An Adaptive Extended Kalman Filter is proposed for Mobile Robot Localization and the first and second moment of odometry sensors noise estimation. | |
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Internacional
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Si |
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Nombre congreso
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40th International Symposium on Robotics (ISR) |
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Tipo de participación
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960 |
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Lugar del congreso
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Barcelona (España) |
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Revisores
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Si |
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ISBN o ISSN
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978-84-920933-8-0 |
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DOI
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Fecha inicio congreso
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10/03/2009 |
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Fecha fin congreso
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13/03/2009 |
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Desde la página
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259 |
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Hasta la página
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264 |
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Título de las actas
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Proceedings Book 40th International Symposium on Robotics (ISR) |