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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorJiang, S-
dc.creatorYao, W-
dc.creatorWong, MS-
dc.creatorHang, M-
dc.creatorHong, ZH-
dc.creatorKim, EJ-
dc.creatorJoo, SH-
dc.creatorKuc, TY-
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
dc.rightsThe following publication S. Jiang et al., "Automatic Elevator Button Localization Using a Combined Detecting and Tracking Framework for Multi-Story Navigation," in IEEE Access, vol. 8, pp. 1118-1134, 2020 is available at
dc.subjectTarget trackingen_US
dc.subjectSimultaneous localization and mappingen_US
dc.subjectTask analysisen_US
dc.subjectMachine learningen_US
dc.subjectElevator button localizationen_US
dc.subjectMulti-story navigationen_US
dc.subjectObject detectionen_US
dc.subjectVisual trackingen_US
dc.subjectDeep learningen_US
dc.titleAutomatic elevator button localization using a combined detecting and tracking framework for multi-story navigationen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractSimultaneous localization and mapping (SLAM) is an important function for service robots to self-navigate modernized buildings. However, only a few existing applications allow them to automatically move between stories through elevator. Some approaches have accomplished with the aid of hardware; however, this study shows that computer vision can be a promising alternative for button localization. In this paper, we proposed a real-time multi-story SLAM system which overcomes the problem of detecting elevator buttons using a localization framework that combines tracking and detecting approaches. A two-stage deep neural network initially locates the original positions of the target buttons, and a part-based tracker follows the target buttons in real-time. A positive-negative classifier and deep learning neural network (particular for button shape detection) modify the tracker's output in every frame. To allow the robot to self-navigate, a 2D grid mapping approach was used for the localization and mapping. Then, when the robot navigates a floor, the A002A; algorithm generates the shortest path. In the experiment, two dynamic scenes (which include common elevator button localization challenges) were used to evaluate the efficiency of our approach, and compared it with other state-of-the-art methods. Our approach was also tested on a prototype robot system to assesses how well it can navigate a multi-story building. The results show that our method could overcome the common background challenges that occur inside an elevator, and in doing so, it enables the mobile robot to autonomously navigate a multi-story building.-
dcterms.bibliographicCitationIEEE access, 6 Dec. 2019, v. 8, p. 1118-1134-
dcterms.isPartOfIEEE access-
dc.description.validate202006 bcrc-
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