Reference scan matching for global self-localization

Especially in dynamic environments a key feature concerning the robustness of mobile robot navigation is the capability of global self-localization. This term denotes a robot’s ability to generate and evaluate position hypotheses independently from initial estimates, by these means providing the capacity to correct position errors of arbitrary scale. In the self-localization frame described in this article, the feature based APR scan matching algorithm provides for each new laser scan several possible alignments within a set of reference scans. The generation of alignments depends on similarities between the current scan and the reference scans, only, and is unconstrained by earlier estimates. The resulting multiple position hypotheses are tracked and evaluated in a hybrid topological/metric world model by a Bayesian approach. This probabilistic technique is especially designed to integrate position information from different sources, e. g. laserscanners, computer vision, etc. .