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研究生: 張義宗
Chang, Yi-Chung
論文名稱: 立體相機以光流速度估測建構即時區域定位技術
Real-Time Local Positioning by Stereo Camera using Optical Flow Velocity Estimation
指導教授: 林清一
Lin, Chin-E
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: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 87
中文關鍵詞: 光流特徵點室內導航速度回饋
外文關鍵詞: Optical Flow, Feature Points, Indoor Navigation, Velocity Feedback
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  • 為了提升無人機的飛行安全,近年來影像處理被置入以提升
    飛行導控精準度,光流法為其中一項影像演算法。光流將系統
    描述為一種物體運動的圖示,藉由光流可以測量出物體平面運
    動之軌跡及速度。本文利用光流法預測相機速度模型,並利用
    立體相機提升特徵點擷取精準度,整合加速規、陀螺儀、磁力
    計以及氣壓計,將光流規格化轉為向北及向東速度,再與GPS
    所測量到的速度比較及分析,利用即時速度回饋解決無人機軌
    跡飄移,也可達成室內導航的目的。

    In order to raise UAV flight safety, visual processing is introduced to
    improve their navigation accuracy in recent years. One of visual processing
    algorithm is Optical Flow. Optical Flow is one of the important algorithm
    which describes the system as pattern of object’s motion. From the pattern,
    object trajectory and horizontal velocity can be measured. The application of
    optical flow velocity estimation is proposed in this thesis. In order to improve
    optical flow accuracy, stereo camera help optical flow capture feature points
    between left and right lens. With integration of accelerometer, gyroscope,
    magnetometer and barometer compensation, the optical flow can be
    transferred and scaled to North and East velocity. Then analysis and
    comparison with NE velocity which measured form GPS, the proposed
    optical flow takes the advantage of real-time velocity feedback to solve the
    problem of UAV trajectory drifting or can be realize for indoor navigation.

    ABSTRACT i 摘要 ii ACKNOWLEDGMENTS iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 1 Introduction 1 1.1 Motivation 1 1.2 Research Background 3 1.2.1 Introduction to Computer Vision 3 1.2.2 Optical Flow 4 1.2.3 GNSS Doppler Velocity 5 1.3 Main Idea 6 1.4 Thesis Outline 9 Chapter 2 10 Visual Algorithm 10 2.1 Visual Odometry 10 2.2 Feature Detection 12 2.2.1 FAST Algorithm 12 2.2.2 BRIEF Algorithm 16 2.2.3 ORB Algorithm 18 2.3 Feature Matching 22 2.3.1 KNN matcher 22 2.4 Feature tracking 24 2.5 Depth Estimation 28 2.5.1 SAD Algorithm 29 2.6 Summary 31 Chapter 3 32 Optical Flow Control System 32 3.1 Reference Frame 32 3.1.1 Body Frame 32 3.1.2 Global Frame 33 3.2 Optical Flow Control 35 3.2.1 Embedded System 35 3.2.2 Stereo camera 36 3.2.3 Optical Flow Remote Control 40 3.3 Flight Control 41 3.3.1 STM32 Microchip 41 3.3.2 9 Axis IMU 42 3.3.3 Ublox GPS 49 3.3.4 Barometer 50 3.3.5 Communication Device 51 3.4 Attitude Heading Reference System (AHRS) 51 Chapter 4 56 Experiment Results 56 4.1 System Structure Overview 56 4.2 Experiment of Flight control 57 4.2.1 Attitude Test 58 4.2.2 Altitude/Height Test 60 4.3 Experiment of Optical Flow 62 4.3.1 Translation Test 65 4.3.2 Rotation Test 68 4.3.3 Outdoor Navigation Test 71 4.4 Summary 77 Chapter 5 78 Conclusion and Future Work 78 5.1 Conclusion 78 5.2 Application and Contribution 79 5.3 Future Work 80 References 82

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