Joint 2-D-3-D Traffic Sign Landmark Data Set for Geo-Localization Using Mobile Laser Scanning Data
This paper presents a framework to build a joint 2-D-3-D traffic sign landmark data set for geo-localization using mobile laser scanning (MLS) data. The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2019-07, Vol.20 (7), p.2550-2565 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | You, Changbin Wen, Chenglu Wang, Cheng Li, Jonathan Habib, Ayman |
| description | This paper presents a framework to build a joint 2-D-3-D traffic sign landmark data set for geo-localization using mobile laser scanning (MLS) data. The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-reflective properties of traffic signs, is developed to accurately extract traffic signs from MLS point clouds. Next, the semantic and spatial properties of the traffic signs (type, location, position, and geometric characteristics) are obtained. Then, a joint 2-D-3-D traffic sign landmark data set is built, and a semantic-spatial organization graph is used to organize the traffic sign data set. Last, based on the traffic sign landmark data set, a geo-localization method for a driving car is proposed to estimate the driving trajectory. It can be used for auxiliary positioning of autonomous vehicles. Experimental results demonstrate the reliability of our proposed method for traffic sign detection and the potential of building 2-D-3-D traffic sign landmark data set for driving trajectory estimation from MLS data. |
| doi_str_mv | 10.1109/TITS.2018.2868168 |
| format | Article |
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The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-reflective properties of traffic signs, is developed to accurately extract traffic signs from MLS point clouds. Next, the semantic and spatial properties of the traffic signs (type, location, position, and geometric characteristics) are obtained. Then, a joint 2-D-3-D traffic sign landmark data set is built, and a semantic-spatial organization graph is used to organize the traffic sign data set. Last, based on the traffic sign landmark data set, a geo-localization method for a driving car is proposed to estimate the driving trajectory. It can be used for auxiliary positioning of autonomous vehicles. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-403c22817448540abd643ab92d3e59c07b8be17e1a52dccad3c6d1a2de2ba5223</citedby><cites>FETCH-LOGICAL-c293t-403c22817448540abd643ab92d3e59c07b8be17e1a52dccad3c6d1a2de2ba5223</cites><orcidid>0000-0002-6189-1236 ; 0000-0003-0502-2156 ; 0000-0001-6075-796X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8478211$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8478211$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>You, Changbin</creatorcontrib><creatorcontrib>Wen, Chenglu</creatorcontrib><creatorcontrib>Wang, Cheng</creatorcontrib><creatorcontrib>Li, Jonathan</creatorcontrib><creatorcontrib>Habib, Ayman</creatorcontrib><title>Joint 2-D-3-D Traffic Sign Landmark Data Set for Geo-Localization Using Mobile Laser Scanning Data</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>This paper presents a framework to build a joint 2-D-3-D traffic sign landmark data set for geo-localization using mobile laser scanning (MLS) data. The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-reflective properties of traffic signs, is developed to accurately extract traffic signs from MLS point clouds. Next, the semantic and spatial properties of the traffic signs (type, location, position, and geometric characteristics) are obtained. Then, a joint 2-D-3-D traffic sign landmark data set is built, and a semantic-spatial organization graph is used to organize the traffic sign data set. Last, based on the traffic sign landmark data set, a geo-localization method for a driving car is proposed to estimate the driving trajectory. It can be used for auxiliary positioning of autonomous vehicles. 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The MLS data include 3-D point clouds and corresponding multi-view images. First, an integrated method, based on a deep learning network and the retro-reflective properties of traffic signs, is developed to accurately extract traffic signs from MLS point clouds. Next, the semantic and spatial properties of the traffic signs (type, location, position, and geometric characteristics) are obtained. Then, a joint 2-D-3-D traffic sign landmark data set is built, and a semantic-spatial organization graph is used to organize the traffic sign data set. Last, based on the traffic sign landmark data set, a geo-localization method for a driving car is proposed to estimate the driving trajectory. It can be used for auxiliary positioning of autonomous vehicles. 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| subjects | Automobiles Autonomous vehicles Datasets Driving Estimation geo-localization joint 2-D-3-D Landmarks Localization Localization method Machine learning mobile laser scanning (MLS) multi-view images Point cloud Scanning Semantics Shape Signs Three dimensional models Three-dimensional displays Traffic control traffic sign Traffic signs Trajectory Trajectory analysis |
| title | Joint 2-D-3-D Traffic Sign Landmark Data Set for Geo-Localization Using Mobile Laser Scanning Data |
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