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研究生: 莊竣超
Chuang, Chun-Chao
論文名稱: 影像識別方法應用於固定翼小型無人飛行載具之3D定位分析
Study on CCD Camera Vision-based 3-D Positioning for Small Fixed-wing Unmanned Aerial Vehicle
指導教授: 蕭飛賓
Hsiao, Fei-Bin
學位類別: 碩士
Master
系所名稱: 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年: 2011
畢業學年度: 99
語文別: 英文
論文頁數: 95
中文關鍵詞: 無人飛行載具影像處理影像定位卡爾曼濾波器
外文關鍵詞: UAV, image processing, image positioning, Kalman filter
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  • 近來攝影機已經廣泛被使用在日常生活中以及學術研究上。經由攝影機所截取的畫面可以應用在交通監測、建築物監測、海巡防護、目標物追蹤…等等。然而大部分的應用,影像分析是相當重要且必要的能力。
    本論文主要研究目的在於建立一套即時動態影像定位系統。無人飛機結合眾多感應器使狀態預估的更準確。但是小型固定翼無人飛機,天生上有載重限制,無法搭載更精密的量測儀器。使得影像識別更為重要。在此將攝影機所及時拍攝到的畫面,透過色彩空間轉換後抓取目標物特徵。最後引入飛機上全球定位系統以及姿態儀狀態,可以立即定出目標物當時所在的位置。此套系統不僅僅可以應用於靜態目標物,甚至是動態目標物也可以。最後引入卡爾曼濾波器,來降低估測距離的誤差量。
    此套定位系統已經在硬體迴路模擬飛行測試以及現實生活靜態目標獲得驗證。

    In recent years, The CCD camera is widely used in daily life and in academic research. The images taken via CCD camera can put to use on traffic monitor, building monitor, coastal border patrol, tracking target and so on. However, the analysis of image is very important and necessary ability in most applications.
    The main purpose of this thesis is to establish a vision-based positioning in real-time. UAVs use multi-sensor fusion to estimate the states accurately. But in small fixed-wing UAVs, it can’t carry more accuracy measurement instruments because the weight of payload has limit in nature. It makes the image recognition be more important. The images captured by camera in real-time use the color space transformation to recognize the feature of target. Finally, including the global position system and attitude of aircraft can immediately determine the location of the target. This system not only can be applied to the static target and even the dynamic. Finally, using the Kalman Filtering is slowing down the noise of measuring distance.
    The positioning system has been verified on flight test in hardware-in-the-loop and the static target in real life.

    中文摘要 I ABSTRACT II ACKNOWLEDGEMENTS III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES IX NOMENCLATURE X 1. INTRODUCTION 1 1.1. Background 1 1.2. Motivation And Objectives 4 1.3. Literature Review 5 1.4. Thesis Outline 7 2. METHODOLOGY 8 2.1. Image Processing 8 2.1.1. Color Space Transform 8 2.1.2. Thresholding 11 2.1.3. Morphology Filtering 14 2.1.4. Center of Gravity 17 2.2. The CCD Camera Intrinsic Parameters 20 2.2.1. CCD Camera Coordinate System 20 2.3. The CCD Camera Extrinsic Parameters 27 2.3.1. Aircraft Body Coordinate System 27 2.3.2. E-N-U Coordinate System 28 2.3.3. Transformation of the Three Coordinate Systems 29 2.4. The Discrete Kalman Filtering 33 2.4.1. State Equations 33 2.4.2. Measurement Equations 35 2.4.3. Discrete Kalman Filtering Algorithm 36 3. SYSTEM OVERVIEW 37 3.1. System Architecture 37 3.2. System Hardware 39 3.2.1. CCD Camera System 43 3.2.2. Onboard Computer System 45 3.2.3. Hardware-In-The-Loop Simulation Environment 47 4. EXPERIMENTAL RESULTS 48 4.1. Visual Positioning System 48 4.1.1. Color Space 48 4.1.2. CCD Camera’s Intrinsic Parameters 50 4.1.3. CCD Camera’s Extrinsic Parameters 53 4.2. Hardware-In-The-Loop Simulation 56 4.2.1. Distance 56 4.2.2. Real-time Positioning 65 4.2.3. Trajectory Estimate 70 5. CONCLUDING REMEARKS 85 5.1. Summary of Contributions 85 5.2. Future Works 86 6. REFERENCES 87 7. APPENDIX 90 8. VITA 95

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