Towards lazy data association in SLAM.

D. Haehnel, W. Burgard, B. Wegbreit, and S. Thrun.

We present a lazy data association algorithm for the simultaneous localization and mapping (SLAM) problem. Our approach uses a tree-structured 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 large-scale 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.

The full paper is available in PDF and gzipped Postscript

@INPROCEEDINGS{Haehnel03c,
  AUTHOR	= {H\"{a}hnel, D. and Burgard, W. and Wegbreit, B. and Thrun, S.},
  TITLE		= {Towards Lazy Data Association in {SLAM}},
  YEAR		= {2003},
  BOOKTITLE = 	 {Proceedings of the 11th International Symposium of Robotics Research (ISRR'03)},
  publisher     = {Springer},
  address       = {Sienna, Italy}  
}