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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 黃乾綱 | |
dc.contributor.author | Yi-Fang Tsai | en |
dc.contributor.author | 蔡宜芳 | zh_TW |
dc.date.accessioned | 2021-06-17T04:24:28Z | - |
dc.date.available | 2019-08-21 | |
dc.date.copyright | 2018-08-21 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-15 | |
dc.identifier.citation | 1. Hu, F., Vision-based Assistive Indoor Localization. 2018.
2. Liu, P., et al., A semi-supervised method for surveillance-based visual location recognition. IEEE transactions on cybernetics, 2017. 47(11): p. 3719-3732. 3. The Visual Place Recognition in Changing Environments Benchmark Dataset. 2015. 4. 陳加容, 基於手持移動裝置之室內空間文字影像擷取. 臺灣大學工程科學及海洋工程學研究所學位論文, 2016: p. 1-60. 5. 黃聰哲, 基於全景控制影像進行室內定位及導航之可行性分析. 2016. 6. 林姝廷, 基於單中心圓柱全景影像之室內定位與建圖. 2016. 7. Wang, E. and W. Yan, iNavigation: an image based indoor navigation system. Multimedia tools and applications, 2014. 73(3): p. 1597-1615. 8. Mishkin, D., J. Matas, and M. Perdoch, Mods: Fast and robust method for two-view matching. Computer Vision and Image Understanding, 2015. 141: p. 81-93. 9. Scaramuzza, D. and F. Fraundorfer, Visual odometry [tutorial]. IEEE robotics & automation magazine, 2011. 18(4): p. 80-92. 10. Harris, C. and M. Stephens. A combined corner and edge detector. in Alvey vision conference. 1988. Citeseer. 11. Shi, J. and C. Tomasi, Good features to track. 1993, Cornell University. 12. Rosten, E. and T. Drummond. Machine learning for high-speed corner detection. in European conference on computer vision. 2006. Springer. 13. Rosten, E., R. Porter, and T. Drummond, Faster and better: A machine learning approach to corner detection. IEEE transactions on pattern analysis and machine intelligence, 2010. 32(1): p. 105-119. 14. Lowe, D.G. Object recognition from local scale-invariant features. in Computer vision, 1999. The proceedings of the seventh IEEE international conference on. 1999. Ieee. 15. Bay, H., T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. in European conference on computer vision. 2006. Springer. 16. Gledhill, D., et al., Panoramic imaging—a review. Computers & Graphics, 2003. 27(3): p. 435-445. 17. Zhang, C., et al. Development of an omni-directional 3D camera for robot navigation. in Advanced Intelligent Mechatronics (AIM), 2012 IEEE/ASME International Conference on. 2012. IEEE. 18. Boult, T.E., et al., Omni-directional visual surveillance. Image and Vision Computing, 2004. 22(7): p. 515-534. 19. Huang, F. and Z.-H. Lin. Stereo panorama imaging and display for 3D VR system. in 2008 Congress on Image and Signal Processing. 2008. IEEE. 20. Huang, J., et al. 6-DOF VR videos with a single 360-camera. in Virtual Reality (VR), 2017 IEEE. 2017. IEEE. 21. Yagi, Y., Omnidirectional sensing and its applications. IEICE Transactions on Information and Systems, 1999. 82(3): p. 568-579. 22. Shum, H. and S.B. Kang. Review of image-based rendering techniques. in Visual Communications and Image Processing 2000. 2000. International Society for Optics and Photonics. 23. Nistér, D., O. Naroditsky, and J. Bergen. Visual odometry. in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. 2004. Ieee. 24. Singh, A., Monocular Visual Odometry. 2015. 25. Fraundorfer, F. and D. Scaramuzza, Visual odometry: Part ii: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine, 2012. 19(2): p. 78-90. 26. Longuet-Higgins, H.C., A computer algorithm for reconstructing a scene from two projections. Nature, 1981. 293(5828): p. 133. 27. Nistér, D., An efficient solution to the five-point relative pose problem. IEEE transactions on pattern analysis and machine intelligence, 2004. 26(6): p. 756-770. 28. Kruppa, E., Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung. 1913: Hölder. 29. Geiger, A., et al., Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 2013. 32(11): p. 1231-1237. 30. Black, P.E. sparse graph. 2008 [cited 2018 7/2]; Available from: https://www.nist.gov/dads/HTML/sparsegraph.html. 31. Fredman, M.L. and R.E. Tarjan, Fibonacci heaps and their uses in improved network optimization algorithms. Journal of the ACM (JACM), 1987. 34(3): p. 596-615. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70224 | - |
dc.description.abstract | 當今室內導航的解決策略主要透過Beacon、Wi-Fi等作三角定位。本論文提出一基於視覺資訊的定位及導航系統,不必額外架設硬體設備,只需藉由室內空間中的環境影像建立參照影像資料庫作為後續檢索空間位置之用。
建立任何定位及導航系統的必要流程之一為環境影像與實體空間位置的對應,本研究透過單眼視覺測程演算法簡化全景影像與空間平面圖疊合所需耗費的人力。 本研究提出之定位系統建立在由全景影像所組成之參照資料庫之上,並且設定使用者透過行動裝置相機拍攝查詢影像來進行定位。本研究藉由MODS進行查詢影像與參照影像資料庫之比對,實驗場景台北車站捷運站B2場景中top 1的定位正確率在同時考慮人工標記答案與鄰近答案下可達61.1%。 在導航系統中,本研究提出以環境全景影像在特定視角所擷取出之局部影像作為導航指示影像,利用導航指示影像引導使用者前往目的地。和使用藍點(blue dot)為導航人機介面的系統相比,真實環境畫面的反應可使使用者更確定自己是否走在正確的路上。 | zh_TW |
dc.description.abstract | Nowadays, indoor positioning and navigation problems are mainly solved by triangulation through beacon, Wi-Fi, etc. This paper proposes a vision-based indoor positioning and navigation system which only requires a reference image database constructed by images of indoor environments for localization, with no necessity to set up hardware devices.
One essential step to build an indoor positioning and navigation system is to correspond panoramic images to real space. Through a monocular visual odometry algorithm, this study reduces labor needed for the correspondence. The positioning system in this study is based on a reference database constituted by panoramic images. Users are required to take a picture of the location of inquiry for localization. This study searches for the corresponding image in the reference database through MODS. Experiments of positioning at MRT Taipei Main Station at B2 show that the accuracy rate of top 1 can reach 61.1% if considering artificially marked answers and approximate answers at the same time. With respect to the navigation system, this study proposes using images extracted from panoramas at a specific angle to guide users to their destinations. Compared with the navigation system using the blue dot as the user-interface, reacting to real images of the surroundings allows users to be more certain whether they are on the right path. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:24:28Z (GMT). No. of bitstreams: 1 ntu-107-R05525060-1.pdf: 5643224 bytes, checksum: 70c36220901f8971bb1107181a1a3ca4 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 x 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 1 1.3 研究貢獻 2 1.4 論文架構 2 第二章 文獻探討 3 2.1 基於視覺的位置辨識(vision-based locating, visual place recognition) 3 2.2 全景相機與全景影像 8 2.2.1 全景影像概述 8 2.2.2 全景影像的製作方式 8 2.3 MODS 11 2.4 單眼視覺測程(monocular visual odometry) 12 第三章 問題定義與實驗資料建置 16 3.1 研究問題定義 16 3.2 實驗場景分析 17 3.3 資料庫資料收集 20 3.4 環境影像與空間平面圖疊合 22 3.4.1 單眼視覺測程演算法分析攝影機移動軌跡 23 3.4.2 視覺測程結果與空間平面道路網ground truth對齊 31 3.5 影格分群 33 3.6 建立Graph 35 第四章 定位與導航(Positioning and Navigation) 36 4.1 系統架構圖 36 4.2 定位實驗 38 4.2.1 定位流程 38 4.2.2 實驗資料收集 39 4.2.3 實驗目的與方法 40 4.2.4 實驗結果與討論 42 4.3 導航實驗 47 4.3.1 最短路徑規劃演算法 47 4.3.2 實驗目的與方法 47 4.3.3 實驗結果與討論 50 第五章 結論與探討 52 參考文獻 54 附錄一 台北車站捷運站B2測試影像拍攝位置 56 附錄二 導航實驗之導航指示影像主畫面 57 附錄三 導航實驗之問卷統計結果 60 | |
dc.language.iso | zh-TW | |
dc.title | 基於全景影片之室內定位及影像導航系統 | zh_TW |
dc.title | Vision-Guided Indoor Positioning and Navigation Based on Spherical Panoramic Videos | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張恆華,洪一平,趙鍵哲 | |
dc.subject.keyword | 室內定位,室內導航,全景影像,單眼視覺測程,影像比對, | zh_TW |
dc.subject.keyword | Indoor positioning,Indoor localization,Indoor navigation,Panoramic images,Monocular visual odometry,Image matching, | en |
dc.relation.page | 61 | |
dc.identifier.doi | 10.6342/NTU201803529 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-15 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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