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dc.contributor.authorKu, CHen_US
dc.contributor.authorTsai, WHen_US
dc.date.accessioned2014-12-08T15:46:32Z-
dc.date.available2014-12-08T15:46:32Z-
dc.date.issued1999-06-01en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://dx.doi.org/10.1109/3477.764877en_US
dc.identifier.urihttp://hdl.handle.net/11536/31303-
dc.description.abstractA vision-based approach to obstacle avoidance for autonomous land vehicle (ALV) navigation in indoor environments is proposed. The approach is based on the use of a pattern recognition scheme, the quadratic classifier, to find collision-free paths in unknown indoor corridor environments. Obstacles treated in this study include the walls of the corridor and the objects that appear in the way of ALV navigation in the corridor. Detected obstacles as well as the two sides of the ALV body are considered as patterns, A systematic method for separating these patterns into two classes is proposed. The two pattern classes are used as the input data to design a quadratic classifier, Finally, the two-dimensional decision boundary of the classifier, which goes through the middle point between the two front vehicle a heels, is taken as a local collision-free path, This approach is implemented on a real ALV and successful navigations confirm the feasibility of the approach.en_US
dc.language.isoen_USen_US
dc.subjectALV navigationen_US
dc.subjectcollision-free pathen_US
dc.subjectcomputer visionen_US
dc.subjectobstacle avoidanceen_US
dc.subjectobstacle detectionen_US
dc.subjectpattern recognitionen_US
dc.subjectquadratic classifieren_US
dc.titleObstacle avoidance for autonomous land vehicle navigation in indoor environments by quadratic classifieren_US
dc.typeArticleen_US
dc.identifier.doi10.1109/3477.764877en_US
dc.identifier.journalIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICSen_US
dc.citation.volume29en_US
dc.citation.issue3en_US
dc.citation.spage416en_US
dc.citation.epage426en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000080371500008-
dc.citation.woscount1-
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