Topological SLAM using neighbourhood information of places

  • Authors:

  • Felix Werner;Frederic Maire;Joaquin Sitte;Howie Choset;Stephen Tully;George Kantor

  • Affiliations:

  • Faculty of Science and Technology, Queensland University of Technology, Brisbane, QLD, Australia and NICTA Queensland Lab, St Lucia, QLD, Australia;Faculty of Science and Technology, Queensland University of Technology, Brisbane, QLD, Australia and NICTA Queensland Lab, St Lucia, QLD, Australia;Faculty of Science and Technology, Queensland University of Technology, Brisbane, QLD, Australia and NICTA Queensland Lab, St Lucia, QLD, Australia;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
  • Year:
  • 2009

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Abstract

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.