Inferring a probability distribution function for the pose of a sensor network using a mobile robot

  • Authors:
  • David Meger;Dimitri Marinakis;Ioannis Rekleitis;Gregory Dudek

  • Affiliations:
  • Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada;Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada;Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada;Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada

  • Venue:
  • ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
  • Year:
  • 2009

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Abstract

In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.