@InProceedings{	  payne2006single-sensor-probabilistic-localization-on-the-seres,
  abstract	= {This paper proposes a novel method for localizing a
		  stationary infrared source of unknown orientation relative
		  to a static docking sensor. This method uses elliptical
		  approximations of likely positions of the infrared source
		  and computes the intersections to find the most probable
		  locations. It takes only a few samples to localize, is
		  easily computed with inexpensive microcontrollers, and is
		  robust to sensor noise. We then compare our approach with
		  two other methods. The first uses a Bayesian filter across
		  a map of discrete locations in the robot's operational
		  workspace to determine the suspected source position. The
		  second also uses a probability distribution map but uses
		  the method described by Elfes in his paper on probabilistic
		  sonar-based mapping and navigation [1]. We show that our
		  approach localizes quickly with a single sensor and is no
		  more computationally demanding than other methods. },
  address	= {Tokyo, Japan},
  author	= {Kenneth Payne and Jacob Everist and Feili Hou and Wei-Min
		  Shen},
  booktitle	= {The 9th Intl.\ Conf.\ Intelligent and Autonomous Systems
		  (IAS-9)},
  month		= {March},
  title		= {Single-Sensor Probabilistic Localization on the {SeReS}
		  Self-Reconfigurable Robot},
  year		= {2006}
}