C. Stachniss, G. Grisetti, and W. Burgard
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
Proc. of Robotics: Science and Systems (RSS)
Abstract
This paper presents an integrated approach to
exploration, mapping, and localization. Our algorithm uses a
highly efficient Rao-Blackwellized particle filter to represent the
posterior about maps and poses. It applies a decision-theoretic
framework which simultaneously considers the uncertainty in
the map and in the pose of the vehicle to evaluate potential
actions. Thereby, it trades off the cost of executing an action with
the expected information gain and takes into account possible
sensor measurements gathered along the path taken by the
robot. We furthermore describe how to utilize the properties
of the Rao-Blackwellization to efficiently compute the expected
information gain. We present experimental results obtained in
the real world and in simulation to demonstrate the effectiveness
of our approach.
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Bibtex
@InProceedings{stachniss05robotics,
TITLE = {Information Gain-based Exploration Using Rao-Blackwellized Particle Filters},
AUTHOR = {Stachniss, C. and Grisetti, G. and Burgard, W.},
BOOKTITLE = {Proc.~of Robotics: Science and Systems (RSS)},
ADDRESS = {Cambridge, MA, USA},
YEAR = {2005}
}