Towards Lazy Data Association in SLAM | Zendy

Dirk Hähnel | Zendy; Sebastian Thrun | Zendy; Ben Wegbreit | Zendy; Wolfram Burgard | Zendy

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Towards Lazy Data Association in SLAM

Author(s) -

Dirk Hähnel,

Sebastian Thrun,

Ben Wegbreit,

Wolfram Burgard

Publication year - 2005

Publication title -

springer tracts in advanced robotics

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.485

H-Index - 49

eISSN - 1610-742X

pISSN - 1610-7438

DOI - 10.1007/11008941_45

Subject(s) - data association , association (psychology) , computer science , simultaneous localization and mapping , representation (politics) , overhead (engineering) , artificial intelligence , bayesian probability , tree (set theory) , scale (ratio) , data mining , mathematics , robot , probabilistic logic , mobile robot , geography , cartography , mathematical analysis , philosophy , epistemology , politics , political science , law , operating system

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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