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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/147033
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147033

    Title: 無人機區域偵查及目標物件定位策略之技術研究
    Technical Research on UAV Area Search and Object Geolocation Strategy
    Authors: 劉益誠
    Liu, Yi-Chen
    Contributors: 劉吉軒
    Liu, Jyi-Shane
    Liu, Yi-Chen
    Keywords: 智慧無人機
    Smart UAV
    Area Search
    2D vision
    Distance Estimation
    Geographic Coordinate Projection
    Coverage Path Planning
    UAV Geolocation Strategy
    Feature Matching
    Date: 2023
    Issue Date: 2023-09-01 15:24:38 (UTC+8)
    Abstract: 無人機在現今產業發展快速,因其相較於需人員搭載的飛行器有更低的
    With the rapid development of unmanned aerial vehicle technology and it’s
    high mobility, low risk for drone pilot.Unmanned Aerial Vehicle have been used
    in a variety of applications.In early stage, UAV was mainly used for military
    purposes.But, with UAV technology became more and more prevalent, UAV widely
    applied on Manufacturing industry, agriculture and film industry.Beside the UAV
    technology, the development of image processing also improve development of
    UAV application.And 2D-vision-based image processing was important especially
    for micro UAV because of it’s weight limit.
    For micro UAV, the commonly equipped sensors are a camera, GPS, compass
    and altimeter. Therefore, this research develop three geolocation strategies using
    the sensors commonly found on micro drones to achieve target detection and
    positioning in different mission environments. Among them, the multi-point
    averaging geolocation strategy focuses on the monocular visual positioning module,
    using plane visual images and sensor data to locate ground target.This strategy
    also calibrate the sensor data to improve the reliability of the positioning results.
    To reduce sensor dependency, a triangulation geolocation strategy was developed
    using trigonometric calculations, successfully reducing the reliance on sensors and
    mitigating the impact of altitude on positioning accuracy. The image matching
    geolocation strategy is based on feature matching techniques to achieve pure imagebased
    positioning tasks, enabling drones to perform positioning tasks even in
    mission environments where sensors may fail.
    Reference: [1] Robot operating system (ros) https://www.ros.org/.
    [2] Ros-mobile http://wiki.ros.org/ros-mobile.
    [3] Rodney Brooks. A robust layered control system for a mobile robot. IEEE Journal on
    Robotics and Automation, 2(1):14–23, 1986.
    [4] John Canny. A computational approach to edge detection. IEEE Transactions on Pattern
    Analysis and Machine Intelligence, 8(6):679–698, 1986.
    [5] D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603–619, 2002.
    [6] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-
    supervised interest point detection and description. Computer Vision and Pattern
    Recognition(CVPR), 2018.
    [7] Yoav Gabriely and Elon Rimon. Spanning-tree based coverage of continuous areas by a
    mobile robot. International Conference on Robotics and Automation, 2:1927–1933, 2001.
    [8] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
    Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks.
    NeurIPS, 27, 2014.
    [9] Fatih Gökçe, Göktürk Üçoluk, Erol ̧Sahin, and Sinan Kalkan. Vision-based detection
    and distance estimation of micro unmanned aerial vehicles. Sensors, 15(9):23805–23846,
    [10] Elder M. Hemerly. Automatic georeferencing of images acquired by uav’s. International
    Journal of Automation and Computing, 11(347–352), 2014.
    [11] Luc Van Gool Herbert Bay, Tinne Tuytelaars. Surf: Speeded up robust features. Computer
    –ECCV, 3951, 2006.
    [12] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation
    with conditional adversarial networks. International Journal of Computer Vision, 2018.
    [13] Charles F. F. Karney. Algorithms for geodesics. Journal of Geodesy, 87:43–55, 2013.
    [14] Ragab Khalil. The accuracy of gis tools for transforming assumed total station surveys to
    real world coordinates. Geographic Information System, 5(486-491), 2013.
    [15] Arturo De la Escalera and Jose María Armingol. Automatic chessboard detection for
    intrinsic and extrinsic camera parameter calibration. Sensors, 10(3)(2027-2044), 2010.
    [16] Chaozhen Lan, Wanjie Lu, Junming Yu, and Qing Xu. Deep learning algorithm for feature
    matching of cross modality remote sensing images. Acta Geodaetica et Cartographica
    Sinica, 50(2):14–23, 2021.
    [17] Zuoyue Li, Jan Dirk Wegner, and Aurelien Lucchi. Topological map extraction from
    overhead images. International Conference on Computer Vision, 2019.
    [18] Xiao Ling, Yongjun Zhang, Jinxin Xiong, Xu Huang, and Zhipeng Chen. An image
    matching algorithm integrating global srtm and image segmentation for multi-source
    satellite imagery. Remote Sensing, 8, 2016.
    [19] D.G Lowe. Distinctive image features from scale-invariant keypoints. International
    Journal of Computer Vision, 60(91–110), 2004.
    [20] L. H. Nam, L. Huang, X. J. Li, and J. F. Xu. An approach for coverage path planning for
    uavs. IEEE 14th International Workshop on Advanced Motion Control, (411-416), 2016.
    [21] Donggeun Oh and Junghee Han. Smart search system of autonomous flight uavs for
    disaster rescue. Sensors, 21(20), 2021.
    [22] Edwin Olson. Apriltag: A robust and flexible visual fiducial system. International
    Conference on Robotics and Automation, 2011.
    [23] Parrot. Anafi https://www.parrot.com/en/drones/anafi.
    [24] Parrot. Bebop2 https://www.parrot.com/en/drones.
    [25] Shashikant Prasad. pix2pix gan for generating maps given satellite images using
    pytorch, https:// medium.com/ @skpd/ pix2pix-gan-for-generating-map-given-satellite-
    [26] Wahyu Rahmaniar, Wen-June Wang, Wahyu Caesarendra, Adam Glowacz, Krzysztof
    Oprz ̨edkiewicz, Maciej Sułowicz, and Muhammad Irfan. Distance measurement
    of unmanned aerial vehicles using vision-based systems in unknown environments.
    Electronics, 10(14), 2021.
    [27] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: an efficient
    alternative to sift or surf. Proceedings of the IEEE International Conference on Computer
    Vision, (2564-2571), 2011.
    [28] Sajid Saleem, Abdul Bais, and Robert Sablatnig. Towards feature points based image
    matching between satellite imagery and aerial photographs of agriculture land. Computers
    and Electronics in Agriculture, 126:12–20, 2016.
    [29] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich.
    Superglue: Learning feature matching with graph neural networks. Computer Vision and
    Pattern Recognition(CVPR), 2020.
    [30] Chris Simpson. Behavior trees for ai: How they work, https://www.gamedeveloper.com/
    [31] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr:
    Detector-free local feature matching with transformers. Computer Vision and Pattern
    Recognition(CVPR), 2021.
    [32] Dengqing Tang, Tianjiang Hu, Zhaowei Ma, Lincheng Shen, and Chongyu Pan. Apriltag
    array-aided extrinsic calibration of camera–laser multi-sensor system. Robotics and
    Biomimetics, 3(13), 2016.
    [33] Jinbiao Yuan, Zhenbao Liu, Yeda Lian, Lulu Chen, Qiang An, Lina Wang, and Bodi Ma.
    Global optimization of uav area coverage path planning based on good point set and genetic
    algorithm. Aerospace, 9(2), 2022.
    [34] Xiaoyue Zhao, Fangling Pu, Zhihang Wang, Hongyu Chen, and Zhaozhuo Xu. Detection,
    tracking, and geolocation of moving vehicle from uav using monocular camera. IEEE
    Access, 7:101160–101170, 2019.
    [35] 佐翼科技. Dx30-w1 https://www.droxotech.com/.
    [36] 內政部國土測繪中心. 國土測繪圖資服務雲 https://maps.nlsc.gov.tw.
    [37] 擎壤科技. Eg2 https://www.earthgen.com.tw/eg2.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753136
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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