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Dense map inference with user-defined priors: from priorlets to scan eigenvariations

de la Puente, Paloma and Censi, Andrea (2011) Dense map inference with user-defined priors: from priorlets to scan eigenvariations. Caltech , Pasadena, CA. (Unpublished)

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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 of methods based on geometric primitives.

Item Type:Report or Paper (Technical Report)
Related URLs:
URLURL TypeDescription ItemConference Paper
Censi, Andrea0000-0001-5162-0398
Additional Information:This work was partially funded by the Spanish Ministry of Science and Technology, DPI2007-66846-c02-01.
Group:Control and Dynamical Systems Technical Reports
Funding AgencyGrant Number
Ministerio de Educación y Ciencia (MEC)DPI2007-66846-c02-01
Record Number:CaltechCDSTR:2011.002
Persistent URL:
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28143
Deposited By: Imported from CaltechCDSTR
Deposited On:22 Feb 2011
Last Modified:20 May 2020 18:41

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