Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617504
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dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorGuojie Luoen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorKaigui Bianen_US
dc.contributor.authorTao Wangen_US
dc.contributor.authorXiaoming Lien_US
dc.date.accessioned2020-05-20T09:18:13Z-
dc.date.available2020-05-20T09:18:13Z-
dc.date.issued2016en_US
dc.identifier.issn1536-1233en_US
dc.identifier.other10.1109/TMC.2016.2550040en_US
dc.identifier.urihttp://localhost/handle/Hannan/148154en_US
dc.identifier.urihttp://localhost/handle/Hannan/617504-
dc.descriptionen_US
dc.description.abstractThe lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity and connection areas between stories are 100 percent correct.en_US
dc.publisherIEEEen_US
dc.relation.haspart7446341.pdfen_US
dc.subjectmulti-story indoor floor plan reconstruction|mobile crowdsensingen_US
dc.titleMulti-Story Indoor Floor Plan Reconstruction via Mobile Crowdsensingen_US
dc.typeArticleen_US
dc.journal.volume15en_US
dc.journal.issue6en_US
dc.journal.titleIEEE Transactions on Mobile Computingen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorGuojie Luoen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorKaigui Bianen_US
dc.contributor.authorTao Wangen_US
dc.contributor.authorXiaoming Lien_US
dc.date.accessioned2020-05-20T09:18:13Z-
dc.date.available2020-05-20T09:18:13Z-
dc.date.issued2016en_US
dc.identifier.issn1536-1233en_US
dc.identifier.other10.1109/TMC.2016.2550040en_US
dc.identifier.urihttp://localhost/handle/Hannan/148154en_US
dc.identifier.urihttp://localhost/handle/Hannan/617504-
dc.descriptionen_US
dc.description.abstractThe lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity and connection areas between stories are 100 percent correct.en_US
dc.publisherIEEEen_US
dc.relation.haspart7446341.pdfen_US
dc.subjectmulti-story indoor floor plan reconstruction|mobile crowdsensingen_US
dc.titleMulti-Story Indoor Floor Plan Reconstruction via Mobile Crowdsensingen_US
dc.typeArticleen_US
dc.journal.volume15en_US
dc.journal.issue6en_US
dc.journal.titleIEEE Transactions on Mobile Computingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7446341.pdf2.04 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorGuojie Luoen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorKaigui Bianen_US
dc.contributor.authorTao Wangen_US
dc.contributor.authorXiaoming Lien_US
dc.date.accessioned2020-05-20T09:18:13Z-
dc.date.available2020-05-20T09:18:13Z-
dc.date.issued2016en_US
dc.identifier.issn1536-1233en_US
dc.identifier.other10.1109/TMC.2016.2550040en_US
dc.identifier.urihttp://localhost/handle/Hannan/148154en_US
dc.identifier.urihttp://localhost/handle/Hannan/617504-
dc.descriptionen_US
dc.description.abstractThe lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity and connection areas between stories are 100 percent correct.en_US
dc.publisherIEEEen_US
dc.relation.haspart7446341.pdfen_US
dc.subjectmulti-story indoor floor plan reconstruction|mobile crowdsensingen_US
dc.titleMulti-Story Indoor Floor Plan Reconstruction via Mobile Crowdsensingen_US
dc.typeArticleen_US
dc.journal.volume15en_US
dc.journal.issue6en_US
dc.journal.titleIEEE Transactions on Mobile Computingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7446341.pdf2.04 MBAdobe PDFThumbnail
Preview File