Abstract
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 anchor point relation (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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
