Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88376
DC Field | Value | Language |
---|---|---|
dc.contributor | Interdisciplinary Division of Aeronautical and Aviation Engineering | en_US |
dc.creator | Lee, MJL | en_US |
dc.creator | Lee, S | en_US |
dc.creator | Ng, HF | en_US |
dc.creator | Hsu, LT | en_US |
dc.date.accessioned | 2020-10-29T01:02:49Z | - |
dc.date.available | 2020-10-29T01:02:49Z | - |
dc.identifier.issn | 1424-8220 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/88376 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Lee MJL, Lee S, Ng H-F, Hsu L-T. Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons. Sensors. 2020; 20(17):4728, is available at https://doi.org/10.3390/s20174728 | en_US |
dc.subject | Autonomous driving | en_US |
dc.subject | Cameras | en_US |
dc.subject | GNSS | en_US |
dc.subject | GPS | en_US |
dc.subject | Image segmentation | en_US |
dc.subject | Land application | en_US |
dc.subject | Localization | en_US |
dc.subject | Navigation | en_US |
dc.subject | Urban canyon | en_US |
dc.title | Skymask matching aided positioning using sky-pointing fisheye camera and 3d city models in urban canyons | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 17 | en_US |
dc.identifier.volume | 20 | en_US |
dc.identifier.issue | 17 | en_US |
dc.identifier.doi | 10.3390/s20174728 | en_US |
dcterms.abstract | 3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors (Switzerland), 2020, v. 20, no. 17, 4728, p. 1-17 | en_US |
dcterms.isPartOf | Sensors (Switzerland) | en_US |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85089716824 | - |
dc.identifier.pmid | 32825673 | - |
dc.identifier.artn | 4728 | en_US |
dc.description.validate | 202010 bcma | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Lee_Skymask_matching_aided.pdf | 4.89 MB | Adobe PDF | View/Open |
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