Topological SLAM using neighbourhood information of places


Autoria(s): Werner, Felix; Maire, Frederic D.; Sitte, Joaquin; Choset, Howie; Tully, Stephen; Kantor, George
Data(s)

15/10/2009

Resumo

Perceptual aliasing makes topological navigation a difficult task. In this paper we present a general approach for topological SLAM~(simultaneous localisation and mapping) which does not require motion or odometry information but only a sequence of noisy measurements from visited places. We propose a particle filtering technique for topological SLAM which relies on a method for disambiguating places which appear indistinguishable using neighbourhood information extracted from the sequence of observations. The algorithm aims to induce a small topological map which is consistent with the observations and simultaneously estimate the location of the robot. The proposed approach is evaluated using a data set of sonar measurements from an indoor environment which contains several similar places. It is demonstrated that our approach is capable of dealing with severe ambiguities and, and that it infers a small map in terms of vertices which is consistent with the sequence of observations.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/26543/

Publicador

IEEE Computer Society

Relação

http://eprints.qut.edu.au/26543/1/c26543.pdf

http://www.iros09.mtu.edu/index.php/IROS_2009:_The_2009_IEEE/RSJ_International_Conference_on_Intelligent_RObots_and_Systems

Werner, Felix, Maire, Frederic D., Sitte, Joaquin, Choset, Howie, Tully, Stephen, & Kantor, George (2009) Topological SLAM using neighbourhood information of places. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 11-15, October, 2009, Hyatt Regency , St. Louis, Missouri.

Direitos

Copyright 2009 IEEE Computer Society

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Fonte

Faculty of Science and Technology

Palavras-Chave #080101 Adaptive Agents and Intelligent Robotics #Mobile Robot #Topological navigation #Simultaneous localisation and mapping (SLAM) #Neighbourhood Information #Particle Filter
Tipo

Conference Paper