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研究生： 許紫琳
Tzu-Lin Hsu
論文名稱： 無人機應用於室內停車場停車引導
指導教授： 王文俊
Wen-June Wang
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 79
中文關鍵詞： 無人機 、室內定位 、路徑規劃 、物件追蹤 、特徵匹配
外文關鍵詞： Drone, Indoor Positioning, Path Planning, Object Tracking, Feature Matching
相關次數： 點閱：17  下載：0 
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    • 汽車進入室內大型停車場，若無任何空車位指引機制，往往要花費許多時間在尋找空車位，若剩餘車位不多，很可能需要繞行多條沒有車位的路段才能找到空車位，既浪費時間，也浪費力氣。因此本論文旨在建立一套無人機之室內停車場引導系統。首先將停車場地圖建入系統，系統會對每一輛進入停車場欲停車的車輛自動選擇距離停車場入口最近的空車位，並規畫出到達停車場入口的最短路徑，無人機將依據此路徑為汽車做引導，引導完成後，無人機將拍攝汽車停放完成的影像回傳給系統更新車位狀態，如此的系統將能為駕駛節省停車時間，同時也提升了停車場管理效率。
    本引導系統包含兩個分支系統，分別是停車場系統以及無人機系統。停車場系統包含了空車位選擇以及路徑規劃，系統會根據當前剩餘空車位，透過A*演算法(A* algorithm)對每一個空車位做評估，進而找到距離停車場入口最近的空車位，並且規畫出無人機引導的最短路徑。無人機系統則包含了室內定位以及引導車輛狀態追蹤方法，由於室內收不到GPS訊號，因此本系統基於AprilTag標籤來協助無人機完成飛行引導任務。另外引導車輛狀態追蹤方法可分為四個功能，第一個功能為車輛偵測，採用Yolov4 tiny輕量化網路模型做車輛偵測，在足夠達到偵測效果的同時減少嵌入式系統的計算資源；第二個功能為汽車追蹤，透過改良之SORT演算法(Simple Online And Realtime Tracking)用以追蹤被引導車輛的動態；第三個功能為汽車特徵比對，當追蹤失敗系統會自動啟動汽車特徵比對的功能，透過ORB演算法(Oriented FAST and Rotated BRIEF)找尋追蹤目標特徵，接著使用FLANN匹配器(Fast Libary for Approximate Nearest Neighbors)做目標配對，最終將目標成功找回；第四個功能為等車機制，無人機在引導期間能夠隨時留意被引導汽車與無人機之間的距離，若離太遠會停止等待汽車跟上，落實引導目的。本論文最終於實際停車場環境驗證本系統的可行性，實測結果在無干擾環境下無人機確實能夠達成所需任務。

    Car driving in the parking garage to find a parking space without any guidance mechanism often takes a lot of time. Maybe need to worry about taking a long detour to find a parking space, which is a long and laborious task, so this paper aims to establish an indoor parking system guided by a drone. First, the parking garage map needs to be built into the system. Then the system automatically screens the parking space closest to the parking garage entrance, plans the shortest path to this parking space, and then guides the car according to this path through the drone. After the guidance mission, the drone will take an image of the car that has completed the parking and send it back to the system to update the parking space status. Such a system can save drivers' parking time and make parking lot management more effective.
    This guidance system consists of two subsystems: the parking garage and drone systems. The parking garage system includes parking space selection and path planning. The system will evaluate each vacant parking space through the A* algorithm to find the target parking space closest to the entrance and plan the shortest path for the drone guidance. The drone system includes indoor positioning and guided car status tracking method. Because the GPS signal cannot be received indoors, the system is based on the AprilTag to assist the drone in completing the flight guidance task. The guided car status tracking method can be divided into four functions. The first is car detection, and the Yolov4 tiny, lightweight network model is used to do car detection, which is sufficient to achieve the detection effect and reduce the computing resources of the embedded system. The second is car tracking, by improving the SORT algorithm(Simple Online And Realtime Tracking) to track guided car dynamics. The third is the search mechanism, and the system will automatically start the function of searching for the tracking target when the tracking fails, find the tracking target feature through the ORB algorithm(Oriented FAST and Rotated BRIEF), and then use the FLANN matcher(Fast Libary for Approximate Nearest Neighbors) to do target pairing, the target will be successfully retrieved finally. The fourth is the waiting mechanism, the drone can keep an eye on the distance between the guided car and the drone at any time during guidance, and if the distance is too far, the drone will stop and wait for the car to follow up to implement the guidance purpose. This paper verifies the system's feasibility in the actual parking garage environment. The measured results show that the drone can achieve the required tasks in a non-interference environment.

    摘要 i Abstract ii 致謝 iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 1 1.3 論文目標 3 1.4 論文架構 4 第二章 系統架構與軟硬體介紹 5 2.1 系統架構 5 2.2 硬體介紹 7 2.2.1 無人機設備介紹 7 2.2.2 停車場系統設備介紹 12 2.3 軟體介紹 13 2.3.1 停車場系統 13 2.3.2 無人機系統 13 2.3.3 模擬環境---Unreal Engine 14 第三章 系統間相互通訊方法 16 3.1 停車場系統與無人機通訊 16 3.1.1 Socket 16 3.2 無人機與周邊設備通訊 18 3.2.1 ROS (Robot Operating System) 18 第四章 停車場系統 20 4.1 停車場系統建立 20 4.2 路徑規劃 20 4.3 路徑規劃介面 23 第五章 室內定位 25 5.1 AprilTag方法 25 5.1.1 前置作業---鏡頭校正 25 5.1.2 使用套件---AprilTag-ROS 25 5.2 AprilTag定位原理 26 5.2.1 AprilTag座標設定 26 5.2.2 AprilTag座標推算無人機位置 27 5.2.3 定位修正 28 5.2.4 旋轉矩陣 30 第六章 被引導車輛狀態追蹤 34 6.1 車輛偵測 34 6.2 汽車追蹤 34 6.2.1 目標位置預測---卡爾曼濾波(Kalman Filter) 37 6.2.2 目標位置匹配---匈牙利演算法(Hungarian algorithm) 37 6.2.3 目標位置更新---卡爾曼濾波(Kalman Filter) 40 6.3 汽車特徵比對 41 6.3.1 特徵提取---oFAST(FASTKeypoint Orientation) 42 6.3.2 特徵描述---rBRIEF(Streed BRIEF) 43 6.3.3 特徵匹配---FLANN(Fast Libary for Approximate Nearest Neighbors) 44 6.4 等車機制 46 6.4.1 不等車情境 47 6.4.2 無人機與被引導車子的距離判斷 47 6.5 拍照機制 48 第七章 實驗結果與討論 49 7.1 車位選擇及路徑規劃 49 7.2 AprilTag定位 51 7.3 被引導車輛狀態追蹤 53 7.3.1 車輛偵測、追蹤及特徵比對 53 7.3.2 等車機制討論 56 7.4 車輛引導測試 56 7.4.1 實驗環境 56 7.4.2 引導期間 57 7.4.3 實驗結果討論 62 第八章 結論與未來展望 63 8.1 結論 63 8.2 未來展望 63 參考文獻 64

    [1] "SmartPark - Intelligent Parking System using Drones," [Online]. Available: https://invent.psu.edu/ip_item/smartpark-intelligent-parking-system-using-drones/. [Accessed: March, 2023].
    [2] "Intelligent Parking Drone Technology Wins Siemens’ First Idea Contest," [Online]. Available: https://erticonetwork.com/intelligent-parking-drone-technology-wins-siemens-first-idea-contest/. [Accessed: March, 2023].
    [3] "Smart Parking Lot Using Quadcopter Network," [Online]. Available: https://www.uasvision.com/2015/03/18/smart-parking-lot-using-quadcopter-network/. [Accessed: March, 2023].
    [4] "Smart Parking Using Drones," [Online]. Available: https://datafromsky.com/news/smart-parking-using-drones/. [Accessed: March, 2023].
    [5] H. Wang, Y. Yu, and Q. Yuan, "Application of Dijkstra Algorithm in Robot Path-Planning," Second International Conference on Mechanic Automation and Control Engineering, 2011, pp. 1067-1069.
    [6] F. Duchoň et al., "Path Planning with Modified A Star Algorithm for a Mobile Robot," Procedia Eng., 2014, vol. 96, pp. 59-69.
    [7] D. Ferguson, M. Likhachev and A. Stentz, "A Guide to Heuristic-Based Path Planning," Proceedings of the International Workshop on Planning under Uncertainty for Autonomous Systems International Conference on Automated Planning and Scheduling, 2005, pp. 918.
    [8] G. Tang, C. Tang, C. Claramunt, X. Hu and P. Zhou, "Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment," IEEE Access, vol. 9, 2021.
    [9] R. Yang and L. Cheng, "Path Planning of Restaurant Service Robot Based on A-Star Algorithms with Updated Weights," The 12th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, 2019.
    [10] L. Cheng, C. Liu and B. Yan, "Improved Hierarchical A-Star Algorithm for Optimal Parking Path Planning of the Large Parking Lot," Proc. IEEE Int. Conf. Inf. Autom., 2014, pp. 695-698.
    [11] L. Yang, X. Feng, J. Zhang and X. Shu, "Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments," Sensors, vol. 19, no. 8, 2019.
    [12] F. Orjales, J. Losada-Pita, A. Paz-Lopez and A. Deibe, "Towards Precise Positioning and Movement of UAVs for Near-Wall Tasks in GNSS-Denied Environmen," Sensors, 2021.
    [13] B. Jang and H. Kim, "Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey," IEEE Commun. Surveys Tuts., vol. 21, no. 1, 2019.
    [14] J. Kallwies, B. Forkel and H.-J. Wuensche, "Determining and Improving the Localization Accuracy of AprilTag Detection," Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 8288-8294.
    [15] Z. Li, Y. Chen, H. Lu, H. Wu and L. Cheng, "UAV Autonomous Landing Technology Based on AprilTags Vision Positioning Algorithm," Proc. Chinese Control Conference, 2019, pp. 8148-8153.
    [16] Q. Li et al., "GIS Room Autonomous Inspection System Based on Multi-Rotor UAV," International Conference on Electrical Materials and Power Equipment, 2021, pp. 1-4.
    [17] A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020.
    [18] E. Bochinski, V. Eiselein and T. Sikora, "High-Speed Tracking-by-Detection Without Using Image Information," The 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017, pp. 1-6.
    [19] S. Pan, Z. Tong, Y. Zhao, Z. Zhao, F. Su and B. Zhuang, "Multi-Object Tracking Hierarchically in Visual Data Taken From Drones," Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019, pp. 135-143.
    [20] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Realtime Tracking," IEEE International Conference on Image Processing (ICIP), 2016, pp. 3464-3468.
    [21] N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric," IEEE International Conference on Image Processing (ICIP), 2017, pp. 3645–3649.
    [22] H. Bay, A. Ess, T. Tuytelaars and L. V. Gool, "Speeded-Up Robust Features (SURF)," Comput. Vis. Image Understanding, vol. 110, no. 3, 2008.
    [23] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, 2004.
    [24] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB：An Efficient Alternative to SIFT or SURF," In International conference on computer vision (ICCV), 2011, pp. 2564-2571.
    [25] B. Zhao, H. Wang, L. Tang and Y. Han, "Towards Long-Term UAV Object Tracking via Effective Feature Matching," Electron. Lett., 2020.
    [26] "ArduPilot - Versatile, Trusted, Open," [Online]. Available: https://ardupilot.org/. [Accessed: March, 2023].
    [27] S. Shah et al., "AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles," arXiv preprint arXiv:1705.05065, 2017.
    [28] L. Kalita, "Socket Programming," International Journal of Computer Science and Information Technologies, 2014, vol. 5, no. 3, pp. 4802-4807.
    [29] J. Postel, "Transmission Control Protocol," Internet Engineering Task Force RFC 793, 1981.
    [30] M. Quigley et al., "ROS: An Open-Source Robot Operating System," ICRA workshop on open source software, vol. 3, no. 3.2, 2009.
    [31] I. Dokmanić, R. Parhizkar, J. Ranieri and M. Vetterli, "Euclidean Distance Matrices: Essential Theory Algorithms and Applications," IEEE Signal Process. Mag., vol. 32, no. 6, 2015.
    [32] R. Korf, "Recent Progress in the Design and Analysis of Admissible Heuristic Functions," In Proceedings of the Seventeenth National Conference on Artificial Intelligence, 2000, pp. 1165-1170.
    [33] E. Olson, "AprilTag: A Robust and Flexible Visual Fiducial System," Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2011, pp. 3400-3407.
    [34] "apriltag_ros," [Online]. Available: http://wiki.ros.org/apriltag_ros. [Accessed: March, 2023].
    [35] J. Li, S. Shi and X. Gu, "A Multi-Source Fused Location Estimation Method for UAV Based on Machine Vision and Strapdown Inertial Navigation," Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 356. 2021.
    [36] J. W. Hsieh, and I. H. Yeh, "CSPNet: A New Backbone That Can Enhance Learning Capability of CNN," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2020, pp. 390-391.
    [37] "YOLOv4-Tiny Released," [Online]. Available: https://github.com/AlexeyAB/darknet/issues/6067. [Accessed: March, 2023].
    [38] G. Welch and G. Bishop, "An Introduction to the Kalman Filter," Technical Report, University of North Carolina at Chapel Hill, 1995.
    [39] H. W. Kuhn, "The Hungarian Method for the assignment Problem," Naval Research Logistics Quarterly, 1955, vol. 2, nos. 1-2, pp. 83-97.
    [40] E. Rosten, R. Porter and T. Drummond, "Faster and Better: A Machine Learning Approach to Corner Detection," IEEE Trans. Pattern Anal. Mach. Intell., 2010, vol. 32, no. 1, pp. 105-119.
    [41] M. Calonder, V. Lepetit, C. Strecha and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," In European Conference on Computer Vision(ECCV), 2010, pp.778-792.
    [42] H. C. van Assen, M. Egmont-Petersen and J. H. C. Reiber, "Accurate Object Localization in Gray Level Images using the Center of Gravity Measure: Accuracy Versus Precision," IEEE Trans. Image Process., 2002, vol. 11, pp. 1379-1384.
    [43] E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt and J. M. Ogden, "Pyramid Methods in Image Processing," RCA Engineer, 1984, vol. 29, no. 6, pp. 33-41.
    [44] "Pyramid (Image Processing)-Wikipedia," [Online]. Available: https://en.wikipedia.org/wiki/Pyramid_(image_processing). [Accessed: March, 2023].
    [45] E. Rosten, R. Porter and T. Drummond, "Flann-Fast Library for Approximate Nearest Neighbors user Manual," IEEE Trans. Pattern Anal. Mach. Intell., 2010, vol. 32, no. 1, pp. 105-119.
    [46] L. E. Peterson, "K-Nearest Neighbor," Scholarpedia, 2009.
    [47] K. Chow and K. Denning, "A Simple Multiple Variance Ratio Test," Journal of Econometrics, 1993, pp. 385-401.
    [48] J. G. Mauldon, "Similar Triangles," Math. Magazine, 39, 1966, pp. 165-174.
    [49] J. Kallwies, B. Forkel and H.-J. Wuensche, "Determining and Improving the Localization Accuracy of AprilTag Detection," Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 8288-8294.
    [50] 許紫琳製作: https://youtu.be/BUnzVE8vcB8.
    [51] 許紫琳製作: https://youtu.be/S5N3edQm0uE.

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