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June 2007 A statistical approach to simultaneous mapping and localization for mobile robots
Anita Araneda, Stephen E. Fienberg, Alvaro Soto
Ann. Appl. Stat. 1(1): 66-84 (June 2007). DOI: 10.1214/07-AOAS115

Abstract

Mobile 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.

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Anita Araneda. Stephen E. Fienberg. Alvaro Soto. "A statistical approach to simultaneous mapping and localization for mobile robots." Ann. Appl. Stat. 1 (1) 66 - 84, June 2007. https://doi.org/10.1214/07-AOAS115

Information

Published: June 2007
First available in Project Euclid: 29 June 2007

zbMATH: 1129.62118
MathSciNet: MR2393841
Digital Object Identifier: 10.1214/07-AOAS115

Keywords: Bayesian models , graphical models , Hidden Markov models , importance sampling , particle filtering , SLAM

Rights: Copyright © 2007 Institute of Mathematical Statistics

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