2.50
Hdl Handle:
http://hdl.handle.net/2436/620402
Title:
An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture
Authors:
Jiménez Serrata, Albert A.; Yang, Shufan ( 0000-0003-0531-2903 ) ; Li, Renfa
Abstract:
The 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.
Citation:
An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture 2017, 2017 (1) EURASIP Journal on Embedded Systems
Publisher:
Springer
Journal:
EURASIP Journal on Embedded Systems
Issue Date:
2-Mar-2017
URI:
http://hdl.handle.net/2436/620402
DOI:
10.1186/s13639-017-0075-9
Additional Links:
http://jes.eurasipjournals.springeropen.com/articles/10.1186/s13639-017-0075-9
Type:
Article
Language:
en
ISSN:
1687-3955
Appears in Collections:
Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorJiménez Serrata, Albert A.en
dc.contributor.authorYang, Shufanen
dc.contributor.authorLi, Renfaen
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 Systemsen
dc.identifier.issn1687-3955en
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.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://jes.eurasipjournals.springeropen.com/articles/10.1186/s13639-017-0075-9en
dc.rightsArchived with thanks to EURASIP Journal on Embedded Systemsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSimultaneous localisation and mappingen
dc.subjectRoboticsen
dc.subjectEmbedded systemsen
dc.subjectPixy cameraen
dc.titleAn intelligible implementation of FastSLAM2.0 on a low-power embedded architectureen
dc.typeArticleen
dc.identifier.journalEURASIP Journal on Embedded Systemsen
dc.date.accepted2017-02-09-
rioxxterms.funderUniversity of Wolverhampton and National Natural Science Foundation of Chinaen
rioxxterms.identifier.project61672217en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-03-07en
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