Abstract
A method to register dynamic and semi-static objects with an a-priori known static map is proposed. Candidates for semi-static objects are extracted from laser data based on a-priori information (shape and size) of common indoor objects. Expectation Maximization serves at the same time scan alignment and data association between scan data andthe static a-priori map and the semi-static map. Dynamic objects are identified as outliers in data association. Evidence of visible but unmatched parts is gathered using recursive Bayesian updates to yield reliable candidate rejection in the presence of sensor noise. This allows the resulting semi-static map to adapt to changes in the environment, which is demonstrated experimentally.
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