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Multi-robot cooperative edge detection using Kalman filtering

dc.citation.epage306en_US
dc.citation.issue4en_US
dc.citation.spage295en_US
dc.citation.volume10en_US
dc.citation.woscount0
dc.contributor.authorSong, KTen_US
dc.contributor.authorChen, HTen_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.date.accessioned2014-12-08T15:39:58Z
dc.date.available2014-12-08T15:39:58Z
dc.date.issued2004en_US
dc.description.abstractThis paper Presents a design and implementation of cooperative edge detection of multiple mobile robots in an indoor environment. We propose a method to fuse sensory data from two mobile robots at different locations based on Kalman filtering techniques. To demonstrate the proposed method, we constructed two mobile robots for experimental purpose. A USB Web camera was put at the front of each robot for environment recognition. linage acquisition and processing are performed onboard the robot exploiting a Linux-based embedded platform. Processed scenic range data from robots are transferred through wireless Ethernet to a robot-home server, where a global representation of the environment is maintained. The constructed map can be accessed at a remote site for tele-operation of the robots through Internet. Each robot can update its knowledge of the world by downloading the map from the server. Experimental results of two-robot cooperative sensing in a test environment are presented to demonstrate the effectiveness of the proposed method.en_US
dc.identifier.issn1079-8587en_US
dc.identifier.journalINTELLIGENT AUTOMATION AND SOFT COMPUTINGen_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/27313
dc.identifier.wosnumberWOS:000224104200004
dc.language.isoen_USen_US
dc.subjectmultiple robotsen_US
dc.subjectmap-buildingen_US
dc.subjectKalman filteringen_US
dc.subjectcooperative sensingen_US
dc.subjectsensor data fusionen_US
dc.subjectedge detectionen_US
dc.subjectimage processingen_US
dc.titleMulti-robot cooperative edge detection using Kalman filteringen_US
dc.typeArticleen_US

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