D. Hähnel, S. Thrun, B. Wegbreit, W. Burgard
Towards Lazy Data Association in SLAM
In Proc. of the International Symposium on Robotics Research
(ISRR), 2003.
Abstract:
We present a lazy data association algorithm
for the simultaneous localization and mapping (SLAM) problem. Our
approach uses a treestructured Bayesian representation of map posteriors
that makes it possible to revise data association decisions arbitrarily
far into the past. We describe a criterion for detecting and repairing
poor data association decisions. This technique makes it possible to
acquire maps of largescale environments with many loops, with a minimum
of computational overhead for the management of multiple data
association hypotheses. A empirical comparison with the popular FastSLAM
algorithm shows the advantage of lazy over proactive data association.
Bibtex:
@InProceedings{haehnel03isrr,
author = {H{\"a}hnel, D. and Thrun, S. and Wegbreit, B. and Burgard, W.},
title = {Towards Lazy Data Association in {SLAM}},
booktitle = {Proc.~of the International Symposium on Robotics Research (ISRR)},
year = {2003}
}
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