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

de la Puente, Paloma and Censi, Andrea (2012) Dense Map Inference with User-Defined Priors: From Priorlets to Scan Eigenvariations. In: Spatial Cognition VIII. Lecture Notes in Computer Science. No.7463. Springer , Berlin, pp. 94-113. ISBN 978-3-642-32731-5.

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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 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.

Item Type:Book Section
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URLURL TypeDescription ReadCube access ItemTechnical Report
Censi, Andrea0000-0001-5162-0398
Additional Information:© 2012 Springer-Verlag Berlin Heidelberg.
Subject Keywords:Penalty Function; Prior Constraint; Inference Engine; Unconstrained Optimization Problem; Consecutive Point
Series Name:Lecture Notes in Computer Science
Issue or Number:7463
Record Number:CaltechAUTHORS:20200520-111908977
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103355
Deposited By: Tony Diaz
Deposited On:20 May 2020 18:41
Last Modified:16 Nov 2021 18:20

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