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dc.contributor.authorJiménez Serrata, Albert A.
dc.contributor.authorYang, Shufan
dc.contributor.authorLi, Renfa
dc.date.accessioned2017-03-07T12:14:33Z
dc.date.available2017-03-07T12:14:33Z
dc.date.issued2017-03-02
dc.identifier.citationAn intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017, 2017 (1) EURASIP Journal on Embedded Systems
dc.identifier.issn1687-3955
dc.identifier.doi10.1186/s13639-017-0075-9
dc.identifier.urihttp://hdl.handle.net/2436/620402
dc.description.abstractThe simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic systems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of processing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer processing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM solution for an embedded processing platform to reduce computer processing time using a low-variance resampling technique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0. Our prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified landmarks in corner detection and corridor detection with only average 1.14 W.
dc.language.isoen
dc.publisherSpringer
dc.relation.urlhttp://jes.eurasipjournals.springeropen.com/articles/10.1186/s13639-017-0075-9
dc.subjectSimultaneous localisation and mapping
dc.subjectRobotics
dc.subjectEmbedded systems
dc.subjectPixy camera
dc.titleAn intelligible implementation of FastSLAM2.0 on a low-power embedded architecture
dc.typeArticle
dc.identifier.journalEURASIP Journal on Embedded Systems
dc.date.accepted2017-02-09
rioxxterms.funderUniversity of Wolverhampton and National Natural Science Foundation of China
rioxxterms.identifier.project61672217
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.licenseref.startdate2017-03-07
refterms.dateFCD2018-10-19T09:23:24Z
refterms.versionFCDVoR
refterms.dateFOA2017-03-07T00:00:00Z
html.description.abstractThe simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic systems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of processing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer processing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM solution for an embedded processing platform to reduce computer processing time using a low-variance resampling technique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0. Our prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified landmarks in corner detection and corridor detection with only average 1.14 W.


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