Active Sensing for Range-Only Mapping using Multiple Hypothesis

L. Merino, F. Caballero, and A. Ollero. Active Sensing for Range-Only Mapping using Multiple Hypothesis. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 37–42, Taipei (Taiwan), October 2010.

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Abstract

Radio signal-based localization and mapping isbecoming more interesting in robotics as applications involvingthe collaboration between robots and static wireless devices aremore common. This paper describes a method for mappingwith a mobile robot the position of a set of nodes using radiosignal measurements. The method employs Gaussian MixturesModels (GMM) for undelayed initialization of the position ofthe wireless nodes within a Kalman filter. Moreover, the paperextends the method to consider active sensing strategies in orderto map the nodes. Entropy variation is used as a measurementof information gain, and allows to prioritize control actions ofthe robot. However, as there is no analytical expression for theentropy of a GMM, upper bounds of the entropy, for whichclose form computation is possible, are used instead. The paperdescribes simulations that show the feasibility of the approach.

BibTeX Entry

@INPROCEEDINGS{merino10iros,
  author = {L. Merino and F. Caballero and A. Ollero},
  title = {Active Sensing for Range-Only Mapping using Multiple Hypothesis},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent
	Robots and Systems (IROS)},
  year = {2010},
  pages = {37--42},
  address = {Taipei (Taiwan)},
  month = {October},
  doi = {10.1109/IROS.2010.5650442},
   abstract={Radio signal-based localization and mapping is
becoming more interesting in robotics as applications involving
the collaboration between robots and static wireless devices are
more common. This paper describes a method for mapping
with a mobile robot the position of a set of nodes using radio
signal measurements. The method employs Gaussian Mixtures
Models (GMM) for undelayed initialization of the position of
the wireless nodes within a Kalman filter. Moreover, the paper
extends the method to consider active sensing strategies in order
to map the nodes. Entropy variation is used as a measurement
of information gain, and allows to prioritize control actions of
the robot. However, as there is no analytical expression for the
entropy of a GMM, upper bounds of the entropy, for which
close form computation is possible, are used instead. The paper
describes simulations that show the feasibility of the approach.},
}

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