政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141713
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51651735      Online Users : 611
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141713


    Title: 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
    Authors: 李尚桀
    Li, Shang-Jie
    Contributors: 甯方璽
    Ning, Fang-Shii
    李尚桀
    Li, Shang-Jie
    Keywords: 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
    Date: 2022
    Issue Date: 2022-09-02 15:20:32 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE transactions on robotics and automation, 12(5), 651-670.
    Hartley, R. I. (1997). In defense of the eight-point algorithm. IEEE Transactions on pattern analysis and machine intelligence, 19(6), 580-593.
    Helmke, U., Hüper, K., Lee, P., & Moore, J. (2004). Essential matrix estimation via Newton-type methods. In 16th International Symposium on Mathematical Theory of Network and System (MTNS), Leuven.
    Hata, K., & Savarese, S. (2017). Cs231a course notes 1: Camera models.
    Jiapeng, Z., Yunjia, W., Xin, L., Xiaoxiang, C., & Hongji, C. (2019). Research on geomagnetic indoor positioning technology. Bulletin of Surveying and Mapping, (1), 18.
    Klein, G., & Murray, D. (2009, October). Parallel tracking and mapping on a camera phone. In 2009 8th IEEE International Symposium on Mixed and Augmented Reality (pp. 83-86). IEEE.
    Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
    Longuet-Higgins, H. C. (1981). A computer algorithm for reconstructing a scene from two projections. Nature, 293(5828), 133-135.
    Li, X., Cheng, G., & Lu, L. (2000). Comparison of spatial interpolation methods. Advances in Earth science, 15(3), 260-265.
    Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
    Le Grand, E., & Thrun, S. (2012, September). 3-axis magnetic field mapping and fusion for indoor localization. In 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 358-364). IEEE.
    Li, B., Gallagher, T., Dempster, A. G., & Rizos, C. (2012, November). How feasible is the use of magnetic field alone for indoor positioning?. In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-9). IEEE.
    Lu, X., Dong, Y., & Wang, X. (2013). A Monte Carlo localization algorithm for 2-D indoor self-localization based on magnetic field. In 2013 8th International Conference on Communications and Networking in China (CHINACOM) (pp. 563-568). IEEE.
    Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189.
    Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., & Furgale, P. (2015). Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3), 314-334.
    Liu, Z., Zhang, L., Liu, Q., Yin, Y., Cheng, L., & Zimmermann, R. (2016). Fusion of magnetic and visual sensors for indoor localization: Infrastructure-free and more effective. IEEE Transactions on Multimedia, 19(4), 874-888.
    Liu, Z. J., Guan, W. G., Hua, H. L., & Sun, Z. D. (2016). Location fingerprint database construction algorithm based on Kriging spatial interpolation. Appl. Res. Comput, 33(10), 3139-3142.
    Luo, H., Zhao, F., Jiang, M., Ma, H., & Zhang, Y. (2017). Constructing an indoor floor plan using crowdsourcing based on magnetic fingerprinting. Sensors, 17(11), 2678.
    Ma, Y., Soatto, S., Košecká, J., & Sastry, S. (2004). An invitation to 3-d vision: from images to geometric models (Vol. 26). New York: springer.
    Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015). ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE transactions on robotics, 31(5), 1147-1163.
    Mo, R. (2016). Studying on comparison of different geomagnetic matching navigation algorithms. Geomatics Spatial Inf. Technol., 39(10), 46-48.
    Mur-Artal, R., & Tardós, J. D. (2017). Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE transactions on robotics, 33(5), 1255-1262.
    Ning, F. S., & Chen, Y. C. (2020). Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation. Sensors, 20(1), 185.
    Panerai, F., Metta, G., & Sandini, G. (2000). Visuo-inertial stabilization in space-variant binocular systems. Robotics and Autonomous Systems, 30(1-2), 195-214.
    Poulose, A., & Han, D. S. (2019). Hybrid indoor localization using IMU sensors and smartphone camera. Sensors, 19(23), 5084.
    Qian, G., Chellappa, R., & Zheng, Q. (2001). Robust structure from motion estimation using inertial data. Josa a, 18(12), 2982-2997.
    Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. Computer Vision–ECCV 2006, 430-443.
    Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011, November). ORB: An efficient alternative to SIFT or SURF. In 2011 International conference on computer vision (pp. 2564-2571). Ieee.
    Ruizhi, C., & Liang, C. (2017). Indoor Positioning with Smartphones: The State-of-the-art and the Challenges. Acta Geodaetica et Cartographica Sinica, 46(10), 1316.
    Schilit, B., Adams, N., & Want, R. (1994, December). Context-aware computing applications. In 1994 First Workshop on Mobile Computing Systems and Applications (pp. 85-90). IEEE.
    Strasdat, H., Montiel, J. M. M., & Davison, A. J. (2010, May). Real-time monocular SLAM: Why filter?. In 2010 IEEE International Conference on Robotics and Automation (pp. 2657-2664). IEEE.
    Storms, W., Shockley, J., & Raquet, J. (2010, October). Magnetic field navigation in an indoor environment. In 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service (pp. 1-10). IEEE.
    Subbu, K. P., Gozick, B., & Dantu, R. (2011, October). Indoor localization through dynamic time warping. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1639-1644). IEEE.
    Saputra, M. R. U., Markham, A., & Trigoni, N. (2018). Visual SLAM and structure from motion in dynamic environments: A survey. ACM Computing Surveys (CSUR), 51(2), 1-36.
    Torres-Solis, J., Falk, T. H., & Chau, T. (2010). A review of indoor localization technologies: towards navigational assistance for topographical disorientation. INTECH Open Access Publisher.
    Tareen, S. A. K., & Saleem, Z. (2018, March). A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. In 2018 International conference on computing, mathematics and engineering technologies (iCoMET) (pp. 1-10). IEEE.
    Weiss, S., Achtelik, M. W., Chli, M., & Siegwart, R. (2012, May). Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV. In 2012 IEEE International Conference on Robotics and Automation (pp. 31-38). IEEE.
    Wu, M., & Yao, J. (2015, June). Adaptive UKF-SLAM based on magnetic gradient inversion method for underwater navigation. In 2015 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 839-843). IEEE.
    Wang, J., Guo, Y., Guo, L., Zhang, B., & Wu, B. (2019). Performance test of MPMD matching algorithm for geomagnetic and RFID combined underground positioning. IEEE Access, 7, 129789-129801.
    Xie, H., Gu, T., Tao, X., Ye, H., & Lu, J. (2015). A reliability-augmented particle filter for magnetic fingerprinting based indoor localization on smartphone. IEEE Transactions on Mobile Computing, 15(8), 1877-1892.
    Xiao, J., Qi, X. H., & Duan, X. S. (2018). Research status of magnetic matching algorithm and its improvement strategies. Electronics Optics & Control, 25(1), 55-59.
    Yeh, S. C., Hsu, W. H., Lin, W. Y., & Wu, Y. F. (2019). Study on an indoor positioning system using Earth’s magnetic field. IEEE Transactions on Instrumentation and Measurement, 69(3), 865-872.
    ZHANG, M., Guangyue, L. U., Honggang, W. A. N. G., & Jiming, L. I. U. (2016). Wireless indoor localization technology based on fingerprint algorithm. Telecommunications Science, 32(10), 77.
    Zhou, X., Chen, T., Guo, D., Teng, X., & Yuan, B. (2018). From one to crowd: A survey on crowdsourcing-based wireless indoor localization. Frontiers of Computer Science, 12(3), 423-450.
     
    二、 網頁參考文獻
    Amy He (2019). People Continue to Rely on Maps and Navigational Apps. Retrieved December 18, 2020, from eMarketer on the World Wide Web：
    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/
    Transparency Market Research (2020). Indoor Location Based Service Market. Retrieved June 29, 2020, from Transparency Market Research on the World Wide Web：
    https://www.transparencymarketresearch.com/
    Andrey Solovev and Anna Petrova (2021). Bluetooth Indoor Positioning Systems: Implementation Features & Alternatives. Retrieved April 06, 2021, from Integra Sources on the World Wide Web：
    https://www.integrasources.com/
    Description: 碩士
    國立政治大學
    地政學系
    109257028
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109257028
    Data Type: thesis
    DOI: 10.6814/NCCU202201193
    Appears in Collections:[Department of Land Economics] Theses

    Files in This Item:

    File Description SizeFormat
    702801.pdf4711KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback