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系統識別號 U0026-0606202010193700
論文名稱(中文) 根據粒子濾波器開發道路線辨識演算法應用於車輛定位及自動循跡功能之研究
論文名稱(英文) Development of a Particle Filter Based Algorithm for Lane Tracking Applied in Vehicle Localization and Autonomous Lane FollowingLane Following
校院名稱 成功大學
系所名稱(中) 航空太空工程學系
系所名稱(英) Department of Aeronautics & Astronautics
學年度 108
學期 2
出版年 109
研究生(中文) 曾晴
研究生(英文) Ching Tseng
電子信箱 sunny567886@gmail.com
學號 P46074278
學位類別 碩士
語文別 英文
論文頁數 67頁
口試委員 指導教授-譚俊豪
口試委員-詹劭勳
口試委員-賴盈誌
中文關鍵字 道路線辨識  道路線追蹤  單眼相機視覺  粒子濾波器  視覺定位  即時定位與建圖 
英文關鍵字 lane detection  lane tracking  mono-vision  Particle Filter  vision-based localization  SLAM 
學科別分類
中文摘要 在道路或線道上移動的自主式地面載具(自走車),除了需要知道自己在全域座標裡的姿態,還需要知道相對於道路邊界地域性的位置,使得自走車能夠保持在線道內。這個問題的困難點在於環境的複雜性太高,在某些情況下,容易造成影像的辨識錯誤。因此,本論文除了實踐基本的影像處理演算法,還提出了修正的方法,來改善道路線的辨識結果。並將粒子濾波器及改進的隨機取樣一致性方法應用於道路線追蹤。而在本論文中,使用三階平滑曲線來描述有著複雜幾何的道路線。同時,由於道路不平整或車體晃動,相機的姿態(包括俯仰角和高度) 也會受到影響,因此也必須被納入估測。此外,融合全球座標系統和上述道路線的資訊作比對,自走車的位置定位的更加準確。最後,將本論文演算法的實驗結果將與單純的影像道路線辨識比較,進行驗證與討論
英文摘要 An autonomous ground vehicle requires not only a system to reliably locate itself in the global coordinate but also the local estimation with respect to the road boundary. One of the challenges will be detecting the lane which accommodates a variety of severe conditions. Hence, this thesis develops an algorithm to track the lanes by applying a Particle Filter with modified RANSAC curve fitting methods, then propagate the measurement in the next image frame. Also, the lane is modeled as a 3rd-order Bezier spline which can describe a complex geometry. Moreover, the primitive parameter of the attitude of the camera, including the pitch angle and the height, are dynamically adjusted because of the unsmooth road condition. Besides, a comparison to the pure lane detection algorithm is verified, which illustrates the improvement and capabilities of this work. In addition, the vehicle tracking problem is discussed where the lane information is used to update the GPS localization.
論文目次 Abstract i
中文摘要 ii
誌謝 iii
Contents iv
List of figures vi
List of Abbreviations ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related work 1
1.3 Goals 3
1.4 Contribution 4
Chapter 2 Methodology (Algorithm infrastructure) 5
Chapter 3 Lane Mark Detection 10
3.1 Inverse Perspective Mapping (IPM) 10
3.2 Image Filtering and Threshold 19
3.3 Lines Detection 20
3.4 RANSAC Spline Fitting 25
3.5 Post-processing 28
3.6 Results 30
3.7 Summary 31
Chapter 4 Lane Tracking 32
4.1 Lane Evolution Model 32
4.2 Particle Filter 36
4.3 Update 37
4.4 Resampling 39
4.5 Pitch and Height Adjustment 41
4.6 Summary 42
Chapter 5 Vehicle Localization 43
5.1 Odometry Motion Model 44
5.2 Update 45
Chapter 6 Experiments 47
6.1 KITTI Dataset 47
6.2 Results 48
6.3 Validation 50
6.4 Pitch and Height Adjustment 57
6.5 Vehicle Localization 59
Chapter 7 Discussion 61
Chapter 8 Conclusions 63
Bibliography 66
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