Intrinsic Localization and Mapping With 2 Applications: Diffusion Mapping and Marco Polo Localization

dc.contributor.author Alegre, Fernando
dc.contributor.author Dellaert, Frank
dc.contributor.author Martinson, Eric Beowulf
dc.contributor.corporatename Georgia Institute of Technology. College of Computing
dc.date.accessioned 2008-05-01T18:39:25Z
dc.date.available 2008-05-01T18:39:25Z
dc.date.issued 2003
dc.description.abstract We investigate Intrinsic Localization and Mapping (ILM) for teams of mobile robots, a multi-robot variant of SLAM where the robots themselves are used as landmarks. We develop what is essentially a straightforward application of Bayesian estimation to the problem, and present two complimentary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in practice. We also provide a discussion of the degrees of freedom and ambiguities in the solution. Finally, we introduce two applications of ILM that bring out its potential: Diffusion Mapping and Marco Polo localization. en_US
dc.identifier.uri http://hdl.handle.net/1853/21301
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Bearings en_US
dc.subject Measurements en_US
dc.subject Odometry en_US
dc.subject Range en_US
dc.subject Sensors en_US
dc.title Intrinsic Localization and Mapping With 2 Applications: Diffusion Mapping and Marco Polo Localization en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Dellaert, Frank
local.contributor.corporatename College of Computing
local.contributor.corporatename Mobile Robot Laboratory
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
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