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題名 同步定位與地圖建構結合磁場地圖提升移動式行動裝置室內定位精度之研究
The Study of Simultaneous Localization and Mapping Integrating Magnetic Field Maps to Improve Indoor Positioning Accuracy of Mobile Devices
作者 李尚桀
Li, Shang-Jie
貢獻者 甯方璽
Ning, Fang-Shii
李尚桀
Li, Shang-Jie
關鍵詞 室內定位
移動式行動裝置
同步定位與地圖建構
磁場定位
Indoor positioning
Mobile devices
Simultaneous localization and mapping
Magnetic field positioning
日期 2022
上傳時間 2-九月-2022 15:20:32 (UTC+8)
摘要 隨著科技不斷的進步，人們對移動式行動裝置的使用量大幅增加，利用其對室內外進行定位與導航的需求也逐漸成長，如何達成更高的定位精度，已是各研究致力於發展的目標。對室外定位而言，全球導航衛星系統的發展已讓室外定位性能趨近完善，然而受限於訊號遮蔽影響，無法有效的對室內使用者進行定位，因此室內定位技術的發展成為近年研究的方向。目前常見的室內定位技術包括，無線射頻、影像視覺及行人航位推算，但各技術的優缺點，使得目前沒有單一室內定位技術能夠解決各項環境因素。
鑒於移動式行動裝置的普及以及運算能力的發展，已能夠大量的處理資料以及快速的分析數據，本研究將以智慧型手機作為實驗裝置，利用移動式行動裝置本身內建之相機與磁力感測器，對室內環境中的特徵與建物結構中磁場之影響量進行資料收集。透過事先建立之室內磁場指紋地圖，以WKNN匹配法獲取初始位置；結合視覺同步定位與地圖建構，利用ORB特徵以推算使用者室內坐標，最後利用耦合及磁場約制將視覺同步定及磁場定位成果結合，並進行精度分析，由研究結果顯示，單一定位技術之定位精度僅1.5至2 m，經耦合並利用磁場確定起始點及約制，精度可達0.5至0.7 m，不同廠牌型號行動裝置以本研究之所提出之方法亦可達到相同之精度。
With the continuous advancement of technology, the use of mobile devices has increased, and the demand for using them for indoor or outdoor positioning has also gradually grown. To achieve higher positioning accuracy has been the goal of various researches. For outdoor positioning, the development of GNSS has improved the outdoor positioning performance. However, due to the influence of signal shielding, it cannot effectively locate indoor users. Therefore, the development of indoor positioning technology has become a target of research in recent years. At present, common indoor positioning technologies include, radio frequency, image vision, and pedestrian dead reckoning. Each technology has its pros and cons which cannot perfectly resolve the issues of indoor positioning technology since various environmental factors would have an impact on it.
In view of the development of mobile devices and computing power, it has been able to process a large amount of data and analyze it rapidly. Therefore, our research will use a smartphone as an experimental device. Using the built-in camera to collect data on the characteristics of the room surroundings, and the magnetometer to detect the influence of the building structure on the magnetic field. First, obtain the initial position by the WKNN matching method through the indoor magnetic field fingerprint map established in advance, and combine the visual simultaneous localization and mapping, the ORB feature is used to calculate the user`s indoor coordinates. Finally use the coupling methods to combine the two positioning results, and perform a precision analysis. The research results show that the positioning accuracy of a single positioning technology is only 1.5 to 2 m. After coupling and using the magnetic field to determine the starting point and constraint, the accuracy can reach 0.5 to 0.7 m. Different brands of mobile devices can also achieve the same accuracy by the method proposed in this study.
參考文獻 一、 外文參考文獻
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Beer, J., Blakemore, C., Previc, F. H., & Liotti, M. (2002). Areas of the human brain activated by ambient visual motion, indicating three kinds of self-movement. Experimental brain research, 143(1), 78-88.
Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer, Berlin, Heidelberg.
Birsan, M. (2010). Recursive Bayesian method for magnetic dipole tracking with a tensor gradiometer. IEEE Transactions on Magnetics, 47(2), 409-415.
Barfoot, T., Forbes, J. R., & Furgale, P. T. (2011). Pose estimation using linearized rotations and quaternion algebra. Acta Astronautica, 68(1-2), 101-112.
Bundak, C. E. A., Abd Rahman, M. A., Abd Karim, M. K., & Osman, N. H. (2022). Effect of Different Signal Weighting Function of Magnetic Field Using KNN for Indoor Localization. In Recent Trends in Mechatronics Towards Industry 4.0 (pp. 571-581). Springer, Singapore.
Corke, P., Lobo, J., & Dias, J. (2007). An introduction to inertial and visual sensing.
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Chulliat, A., Macmillan, S., Alken, P., Beggan, C., Nair, M., Hamilton, B., ... & Thomson, A. (2015). The US/UK world magnetic model for 2015-2020.
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https://www.emarketer.com/
Simon O`Dea (2021). Number of smartphone users from 2016 to 2021. Retrieved August 6, 2020, from Statista on the World Wide Web：
https://www.statista.com/
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描述 碩士
國立政治大學
地政學系
109257028
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109257028
資料類型 thesis
dc.contributor.advisor 甯方璽zh_TW
dc.contributor.advisor Ning, Fang-Shiien_US
dc.contributor.author (作者) 李尚桀zh_TW
dc.contributor.author (作者) Li, Shang-Jieen_US
dc.creator (作者) 李尚桀zh_TW
dc.creator (作者) Li, Shang-Jieen_US
dc.date (日期) 2022en_US
dc.date.accessioned 2-九月-2022 15:20:32 (UTC+8)-
dc.date.available 2-九月-2022 15:20:32 (UTC+8)-
dc.date.issued (上傳時間) 2-九月-2022 15:20:32 (UTC+8)-
dc.identifier (其他 識別碼) G0109257028en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141713-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 109257028zh_TW
dc.description.abstract (摘要) 隨著科技不斷的進步，人們對移動式行動裝置的使用量大幅增加，利用其對室內外進行定位與導航的需求也逐漸成長，如何達成更高的定位精度，已是各研究致力於發展的目標。對室外定位而言，全球導航衛星系統的發展已讓室外定位性能趨近完善，然而受限於訊號遮蔽影響，無法有效的對室內使用者進行定位，因此室內定位技術的發展成為近年研究的方向。目前常見的室內定位技術包括，無線射頻、影像視覺及行人航位推算，但各技術的優缺點，使得目前沒有單一室內定位技術能夠解決各項環境因素。
鑒於移動式行動裝置的普及以及運算能力的發展，已能夠大量的處理資料以及快速的分析數據，本研究將以智慧型手機作為實驗裝置，利用移動式行動裝置本身內建之相機與磁力感測器，對室內環境中的特徵與建物結構中磁場之影響量進行資料收集。透過事先建立之室內磁場指紋地圖，以WKNN匹配法獲取初始位置；結合視覺同步定位與地圖建構，利用ORB特徵以推算使用者室內坐標，最後利用耦合及磁場約制將視覺同步定及磁場定位成果結合，並進行精度分析，由研究結果顯示，單一定位技術之定位精度僅1.5至2 m，經耦合並利用磁場確定起始點及約制，精度可達0.5至0.7 m，不同廠牌型號行動裝置以本研究之所提出之方法亦可達到相同之精度。		zh_TW
dc.description.abstract (摘要) With the continuous advancement of technology, the use of mobile devices has increased, and the demand for using them for indoor or outdoor positioning has also gradually grown. To achieve higher positioning accuracy has been the goal of various researches. For outdoor positioning, the development of GNSS has improved the outdoor positioning performance. However, due to the influence of signal shielding, it cannot effectively locate indoor users. Therefore, the development of indoor positioning technology has become a target of research in recent years. At present, common indoor positioning technologies include, radio frequency, image vision, and pedestrian dead reckoning. Each technology has its pros and cons which cannot perfectly resolve the issues of indoor positioning technology since various environmental factors would have an impact on it.
In view of the development of mobile devices and computing power, it has been able to process a large amount of data and analyze it rapidly. Therefore, our research will use a smartphone as an experimental device. Using the built-in camera to collect data on the characteristics of the room surroundings, and the magnetometer to detect the influence of the building structure on the magnetic field. First, obtain the initial position by the WKNN matching method through the indoor magnetic field fingerprint map established in advance, and combine the visual simultaneous localization and mapping, the ORB feature is used to calculate the user`s indoor coordinates. Finally use the coupling methods to combine the two positioning results, and perform a precision analysis. The research results show that the positioning accuracy of a single positioning technology is only 1.5 to 2 m. After coupling and using the magnetic field to determine the starting point and constraint, the accuracy can reach 0.5 to 0.7 m. Different brands of mobile devices can also achieve the same accuracy by the method proposed in this study.		en_US
dc.description.tableofcontents 謝誌 I
摘要 II
Abstract III
目錄 IV
圖目錄 V
表目錄 VII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究架構 5
第二章 文獻回顧 6
第一節 同步定位與地圖建構 8
第二節 磁場定位技術 18
第三節 室內定位技術結合 30
第三章 研究方法 35
第一節 研究範圍與工具 35
第二節 研究流程 39
第三節 研究方法與理論基礎 41
第四章 研究成果與分析 48
第一節 訓練階段成果分析 48
第二節 單一定位技術及耦合 57
第三節 約制校正實驗成果分析 66
第五章 結論與建議 81
第一節 結論 81
第二節 建議 83
參考文獻 84		zh_TW
dc.format.extent 4824392 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109257028en_US
dc.subject (關鍵詞) 室內定位zh_TW
dc.subject (關鍵詞) 移動式行動裝置zh_TW
dc.subject (關鍵詞) 同步定位與地圖建構zh_TW
dc.subject (關鍵詞) 磁場定位zh_TW
dc.subject (關鍵詞) Indoor positioningen_US
dc.subject (關鍵詞) Mobile devicesen_US
dc.subject (關鍵詞) Simultaneous localization and mappingen_US
dc.subject (關鍵詞) Magnetic field positioningen_US
dc.title (題名) 同步定位與地圖建構結合磁場地圖提升移動式行動裝置室內定位精度之研究zh_TW
dc.title (題名) The Study of Simultaneous Localization and Mapping Integrating Magnetic Field Maps to Improve Indoor Positioning Accuracy of Mobile Devicesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、 外文參考文獻
Andrew, A. M. (2001). Multiple view geometry in computer vision. Kybernetes.
Beer, J., Blakemore, C., Previc, F. H., & Liotti, M. (2002). Areas of the human brain activated by ambient visual motion, indicating three kinds of self-movement. Experimental brain research, 143(1), 78-88.
Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer, Berlin, Heidelberg.
Birsan, M. (2010). Recursive Bayesian method for magnetic dipole tracking with a tensor gradiometer. IEEE Transactions on Magnetics, 47(2), 409-415.
Barfoot, T., Forbes, J. R., & Furgale, P. T. (2011). Pose estimation using linearized rotations and quaternion algebra. Acta Astronautica, 68(1-2), 101-112.
Bundak, C. E. A., Abd Rahman, M. A., Abd Karim, M. K., & Osman, N. H. (2022). Effect of Different Signal Weighting Function of Magnetic Field Using KNN for Indoor Localization. In Recent Trends in Mechatronics Towards Industry 4.0 (pp. 571-581). Springer, Singapore.
Corke, P., Lobo, J., & Dias, J. (2007). An introduction to inertial and visual sensing.
Chung, J., Donahoe, M., Schmandt, C., Kim, I. J., Razavai, P., & Wiseman, M. (2011, June). Indoor location sensing using geo-magnetism. In Proceedings of the 9th international conference on Mobile systems, applications, and services (pp. 141-154).
Chulliat, A., Macmillan, S., Alken, P., Beggan, C., Nair, M., Hamilton, B., ... & Thomson, A. (2015). The US/UK world magnetic model for 2015-2020.
Campos, C., Elvira, R., Rodríguez, J. J. G., Montiel, J. M., & Tardós, J. D. (2021). ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM. IEEE Transactions on Robotics.
Engel, J., Koltun, V., & Cremers, D. (2017). Direct sparse odometry. IEEE transactions on pattern analysis and machine intelligence, 40(3), 611-625.
Filipenko, M., & Afanasyev, I. (2018, September). Comparison of various slam systems for mobile robot in an indoor environment. In 2018 International Conference on Intelligent Systems (IS) (pp. 400-407). IEEE.
Fang, Z. Y., Yuan L., Hou A. P. (2019). Study on indoor positioning based on monocular and odometer combination. Modular Machine Tool & Automatic Manufacturing Technique, 91-94.
Grisetti, G., Kümmerle, R., Stachniss, C., & Burgard, W. (2010). A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2(4), 31-43.
Hamilton, W. R. (1866). Elements of quaternions. London: Longmans, Green, & Company.
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dc.identifier.doi (DOI) 10.6814/NCCU202201193en_US