Inferring a Probability Distribution Function for the Pose of a Sensor Network using a Mobile Robot

David Paul Meger, Dimitri Marinakis, Ioannis Rekleitis, Gregory Dudek
In IEEE International Conference on Robotics and Automation (ICRA) 2009.

Abstract

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.

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BibTeX

@inproceedings{RekleitisICRA2009b,
  author       = {David Paul Meger and Dimitri Marinakis and Ioannis
		 Rekleitis and Gregory Dudek},
  title        = {Inferring a Probability Distribution Function for the
		 Pose of a Sensor Network using a Mobile Robot},
  booktitle    = {IEEE International Conference on Robotics and Automation
		 (ICRA)},
  pages        = {756-762},
  year	       = {2009},
  address      = {Kobe, Japan},
  month        = {May}
}

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