Enhancing 3D range image measurement density via dynamic Papoulis-Gerchberg algorithm

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dc.citation.epage4420en_US
dc.citation.issueNumber16en_US
dc.citation.spage4407en_US
dc.citation.volumeNumber40en_US
dc.contributor.authorKuzucu, E.en_US
dc.contributor.authorÖztürk, D.en_US
dc.contributor.authorGül, M.en_US
dc.contributor.authorÖzbay, B.en_US
dc.contributor.authorArisoy, A. M.en_US
dc.contributor.authorSirin, H. O.en_US
dc.contributor.authorUyanik, I.en_US
dc.date.accessioned2019-02-21T16:07:04Z
dc.date.available2019-02-21T16:07:04Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractAs one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis-Gerchberg (P-G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P-G algorithm impedes its use for online applications. In this study, we advanced the P-G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:07:04Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Scientific and Technological Council of Turkey (TÜBİTAK) and Aselsan A.Sx.
dc.identifier.doi10.1177/0142331218759899
dc.identifier.issn0142-3312
dc.identifier.urihttp://hdl.handle.net/11693/50345
dc.language.isoEnglish
dc.publisherSAGE Publications
dc.relation.isversionofhttps://doi.org/10.1177/0142331218759899
dc.source.titleTransactions of the Institute of Measurement and Controlen_US
dc.subject3D lidaren_US
dc.subjectIterative closest pointen_US
dc.subjectMeasurement densityen_US
dc.subjectPapoulis-Gerchbergen_US
dc.subjectRange sensingen_US
dc.titleEnhancing 3D range image measurement density via dynamic Papoulis-Gerchberg algorithmen_US
dc.typeArticleen_US

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