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Volume 21 Issue 04 - Publication Date: 1 April 2002
 
Robust Mapping and Localization in Indoor Environments Using Sonar Data
 
Juan D. Tardós and José Neira Dept. Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain , Paul M. Newman and John J. Leonard MIT Dept. of Ocean Engineering, USA
 
In this paper we describe a new technique for the creation of feature-based stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison.
 
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