Dense Map Inference with User-Defined Priors: From Priorlets to Scan Eigenvariations

Puente Yusty, Paloma de la and Censi, Andrea (2012). Dense Map Inference with User-Defined Priors: From Priorlets to Scan Eigenvariations. In: "Spatial Cognition VIII", 01/09/2012 - 03/09/2012, Kloster Seeon, Bavaria, Alemania. ISBN 978-3-642-32732-2_6. pp. 94-113.

Description

Title: Dense Map Inference with User-Defined Priors: From Priorlets to Scan Eigenvariations
Author/s:
  • Puente Yusty, Paloma de la
  • Censi, Andrea
Item Type: Presentation at Congress or Conference (Article)
Event Title: Spatial Cognition VIII
Event Dates: 01/09/2012 - 03/09/2012
Event Location: Kloster Seeon, Bavaria, Alemania
Title of Book: Spatial Cognition VIII. Lecture Notes in Computer Science
Date: 2012
ISBN: 978-3-642-32732-2_6
Volume: 7463
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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 working with a particular representation. We describe a system that supports inference with multiple priors while keeping the same dense representation. 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 intrinsic degrees of freedom of the environment’s shape to be recovered. Experiments with simulated and real data sets will be presented

More information

Item ID: 13703
DC Identifier: http://oa.upm.es/13703/
OAI Identifier: oai:oa.upm.es:13703
Deposited by: Memoria Investigacion
Deposited on: 20 Nov 2012 10:37
Last Modified: 21 Apr 2016 13:02
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