Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Hähnel, D., Thrun, S., Wegbreit, B., Burgard, W. (2005). Towards Lazy Data Association in SLAM. In: Dario, P., Chatila, R. (eds) Robotics Research. The Eleventh International Symposium. Springer Tracts in Advanced Robotics, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11008941_45
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DOI: https://doi.org/10.1007/11008941_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23214-8
Online ISBN: 978-3-540-31508-7
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