A statistical approach to simultaneous mapping and localization for mobile robots

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dc.creatorAraneda, Anita
dc.creatorFienberg, Stephen E.
dc.creatorSoto, Alvaro
dc.date2007-08-31
dc.date.accessioned2026-07-07T08:28:57Z
dc.date.available2026-07-07T08:28:57Z
dc.descriptionMobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through an environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range finder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based on importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at Carnegie Mellon University and the Pontificia Universidad Catolica de Chile.
dc.descriptionPublished at http://dx.doi.org/10.1214/07-AOAS115 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0708.4337
dc.identifierhttp://arxiv.org/abs/0708.4337
dc.identifierAnnals of Applied Statistics 2007, Vol. 1, No. 1, 66-84
dc.identifierdoi:10.1214/07-AOAS115
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/137798
dc.subjectApplications
dc.titleA statistical approach to simultaneous mapping and localization for mobile robots
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