D. Hähnel, W. Burgard, D. Fox, and S. Thrun
A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements
In Proc. of the IEEE Conference on Intelligent Robots and Systems
(IROS), 2003.
Abstract:
The ability to learn a consistent model of its
environment is a prerequisite for autonomous mobile robots. A
particularly challenging problem in acquiring environment maps is that
of closing loops; loops in the environment create challenging data
association problems [9]. This paper presents a novel algorithm that
combines Rao- Blackwellized particle filtering and scan matching. In our
approach scan matching is used for minimizing odometric errors during
mapping. A probabilistic model of the residual errors of scan matching
process is then used for the resampling steps. This way the number of
samples required is seriously reduced. Simultaneously we reduce the
particle depletion problem that typically prevents the robot from
closing large loops. We present extensive experiments that illustrate
the superior performance of our approach compared to previous
approaches.
Bibtex:
@InProceedings{haehnel03iros,
author = {H{\"a}hnel, D. and Burgard, W. and Fox, D. and Thrun, S.},
title = {A highly efficient {FastSLAM} algorithm for generating cyclic maps of large-scale environments from raw laser range measurements},
booktitle = {Proc.~of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS)},
year = {2003}
}
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