C. Stachniss, G. Grisetti, and W. Burgard
Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM after Actively Closing Loops
Proc. of the IEEE International Conference on Robotics and Automation (ICRA)
Abstract
Acquiring models of the environment belongs to the fundamental
tasks of mobile robots. Approaches addressing the problem of simultaneous
localization and mapping (SLAM) typically process the perceived sensor data
and do not influence the motion of the mobile robot. In this paper, we present
an approach to actively closing loops during exploration. It applies a
Rao-Blackwellized particle filter to maintain multiple hypotheses about
potential trajectories of the robot and corresponding maps. To prevent the
particle filter from becoming overly confident, we present a technique to
recover the particle diversity after successfully closing a loop. This way the
particle depletion problem is avoided. The combination of our approach with
the active loop closing strategy allows to deal with multiple nested
loops. Experimental results presented in this paper illustrate the advantage
of our method over pervious approaches to mapping with Rao-Blackwellized
particle filters.
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Bibtex
@string{ICRA = "Proc. of the IEEE International Conference on Robotics and Automation (ICRA)"}
@InProceedings{stachniss05icra,
TITLE = {Recovering Particle Diversity in a Rao-Blackwellized
Particle Filter for SLAM after Actively Closing Loops},
AUTHOR = {Stachniss, C. and Grisetti, G. and Burgard, W.},
BOOKTITLE = ICRA,
YEAR = {2005},
PAGES = {667--672},
}