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A General Gaussian-Mixture Approach for Range-only Mapping using Multiple Hypotheses

F. Caballero, L. Merino, and A. Ollero. A General Gaussian-Mixture Approach for Range-only Mapping using Multiple Hypotheses. In Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 4404–4409, Anchorage, Alaska (USA), May 2010.

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Abstract

Radio signal-based localization and mapping isbecoming more interesting as applications involving the collaborationbetween robots and static wireless devices are morecommon. Under certain assumptions, the problem is basicallyequivalent to the range-only localization and mapping problem.The paper presents a method for mapping with a mobile robotthe position of a set of nodes using radio signal measurements.It uses Gaussian Mixtures for undelayed initialization of theposition of the wireless nodes. The paper shows how theapproach can be integrated within a Kalman Filter. This way,information can be used in the filter since the first measurement.The paper describes simulations to verify the feasibility of theapproach, and presents results obtained with experimental datainvolving one mobile robot and a wireless sensor network.

BibTeX Entry

@INPROCEEDINGS{caballero:icra10,
  author = {F. Caballero and L. Merino and A. Ollero},
  title = {A General Gaussian-Mixture Approach for Range-only Mapping using
	Multiple Hypotheses},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and
	Automation, ICRA},
  year = {2010},
  pages = {4404--4409},
  address = {Anchorage, Alaska (USA)},
  month = {May},
  doi = {10.1109/ROBOT.2010.5509704},
   abstract={Radio signal-based localization and mapping is
becoming more interesting as applications involving the collaboration
between robots and static wireless devices are more
common. Under certain assumptions, the problem is basically
equivalent to the range-only localization and mapping problem.
The paper presents a method for mapping with a mobile robot
the position of a set of nodes using radio signal measurements.
It uses Gaussian Mixtures for undelayed initialization of the
position of the wireless nodes. The paper shows how the
approach can be integrated within a Kalman Filter. This way,
information can be used in the filter since the first measurement.
The paper describes simulations to verify the feasibility of the
approach, and presents results obtained with experimental data
involving one mobile robot and a wireless sensor network.}
}

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