Descripción
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When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environment arises naturally. However, so far, the use of a particular structure prior has been coupled to work- ing with a particular representation. We describe a system that supports inference with multiple priors while keeping the same dense representa- tion. The priors are rigorously described by the user in a domain-specific language. Even though we work very close to the measurement space, we are able to represent structure constraints with the same expressivity as methods based on geometric primitives. This approach allows the in- trinsic degrees of freedom of the environment?s shape to be recovered. Experiments with simulated and real data sets will be presented. | |
Internacional
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Si |
Nombre congreso
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Spatial Cognition VIII |
Tipo de participación
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960 |
Lugar del congreso
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Kloster Seeon, Bavaria, Germany |
Revisores
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Si |
ISBN o ISSN
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978-3-642-32731-5 |
DOI
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Fecha inicio congreso
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01/09/2012 |
Fecha fin congreso
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03/09/2012 |
Desde la página
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94 |
Hasta la página
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113 |
Título de las actas
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Spatial Cognition VIII. LNCS, vol. 7463. Springer-Verlag (2012) |