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研究生： 許晋嘉
Hsu, Chin-Chia
論文名稱： 慣性導航核心之自駕車多感測器定位模組：結合全球衛星導航、慣性感測器、單目相機與高精向量地圖
An INS-Centric Multi-Sensor Locator for Autonomous Vehicles Using GNSS, IMU, Monocular Camera, and HD Vector Maps
指導教授： 江凱偉
Chiang, Kai-Wei
學位類別： 碩士
Master
系所名稱： 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 234
中文關鍵詞： 定位模組 、多感測器 、慣性導航 、全球衛星導航 、單目相機 、高精向量地圖
外文關鍵詞： Locator, Multi-sensor, Inertial Navigation System, Global Navigation Satellite System, Monocular Camera, HD Vector Maps
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    • 本論文針對自駕車在不同GNSS條件下的定位需求，提出一套以慣性導航系統（INS）為核心的多感測器（Multi-sensor）定位模組（locator），目標為提供車道內等級之穩定且可靠的定位結果。傳統的全球衛星導航系統與慣性導航系統（GNSS/INS）、視覺為主（Vision-based）或光達為主（LiDAR-based）之定位模組，多半僅能在良好環境下達到理想精度，當車輛行經都會巷弄、高架橋下或GNSS遮蔽區時，定位精度往往會急遽下降。為克服此一限制，本研究提出「locator」概念，使整個導航系統表現如同一顆經強化的GNSS模組，輸出高精度且穩定的位置、速度與姿態（PVA）解，而非各感測器觀測量的組合。
    本論文所提出之定位模組稱為PointLoc，採用INS-centric架構的擴增卡曼濾波器（EKF）實現。INS負責核心的狀態預測，而GNSS、單目相機與IMU之視覺慣性里程計（VIO）、以及高精向量地圖（HD Vector Maps）之地圖匹配，則以鬆耦合（LC）模組的形式整合進同一套濾波架構中，並分別具備對應的量測模型與品質檢核機制。當GNSS訊號可用時，可提供具全球基準的絕對位置更新；VIO模組則提供局部連續且一致的速度估計，在GNSS品質下降期間穩定整體解；高精向量地圖模組則利用相機影像與Lanelet2格式之HD Maps進行匹配，並將成功匹配結果轉換為對位置與航向的車道層級約束。透過此模組化設計，PointLoc能在GNSS/INS、GNSS/INS/VIO，以及GNSS/INS/VIO/HD Vector Maps等不同感測器組合下運作，提供上層規劃（Planning）與控制（Control）模組一致的PVA資訊。
    本研究於台中水湳與台南沙崙兩處代表性測試路線上進行實車實驗，路線包含開闊區、GNSS挑戰區以及GNSS遮蔽區，且具部分高精向量地圖覆蓋。實驗結果顯示，在可用地圖輔助之路段，PointLoc的二維與三維位置均方根誤差可維持在分米等級，同時相較於傳統GNSS/INS架構，顯著降低垂直方向誤差與較大尺度的位置誤差，尤其在GNSS品質不佳或完全遮蔽的情境下更為明顯。在缺乏高精向量地圖的區域，PointLoc會修正為GNSS/INS/VIO定位模組，同時持續提供穩定且連續的導航解。綜合上述結果，本論文證實以INS為核心，結合GNSS、IMU、單目相機與高精向量地圖之模組化整合策略，可實現車道等級之定位精度。

    This thesis investigates an INS-centric multi-sensor locator for autonomous vehicles that aims to provide reliable lane-level localization under varying GNSS conditions. Conventional GNSS/INS, vision-based, or LiDAR-based solutions typically achieve good accuracy only in favorable environments and can degrade sharply in urban canyons, under elevated structures, or during GNSS outages. To address this limitation, the thesis introduces the concept of a locator that appears to the vehicle as a strengthened GNSS module, delivering a single consistent position, velocity, and attitude solution instead of separate sensor outputs.
    The proposed locator, named PointLoc, is implemented as an INS-centric error state extended Kalman filter (EKF). The INS provides the core state propagation, while GNSS, visual-inertial odometry (VIO) from a monocular camera and IMU, and HD Vector Map matching are incorporated as loosely coupled (LC) aiding modules with dedicated measurement models and quality checks. GNSS supplies globally referenced position when signals are available. The VIO module contributes locally consistent velocity estimates that stabilize the solution during periods of degraded GNSS. The HD Vector Map module performs camera to map matching against lane-level features represented in HD Maps in Lanelet2 format and converts successful matches into position and heading constraints. This modular structure allows PointLoc to operate with different combinations of sensors, including GNSS/INS, GNSS/INS with VIO, and GNSS/INS with VIO and HD Vector Maps, while always providing a unified PVA interface to the planning and control stack.
    PointLoc is evaluated using real vehicle experiments on two representative routes in Taichung Shuinan and Tainan Shalun that contain a mix of open sky, GNSS challenging, and GNSS denied segments with partial HD Map coverage. The results show that PointLoc achieves decimeter level 2D and 3D root mean square position errors and reduces vertical error and large error outliers compared with conventional GNSS/INS configurations, particularly in segments with degraded or absent GNSS and available HD Vector Maps. In regions without HD Maps, PointLoc naturally converges toward the GNSS/INS/VIO performance while maintaining a stable navigation output. These findings demonstrate that an INS-centric, modular integration of GNSS, IMU, monocular camera, and HD Vector Maps is a practical and effective strategy for realizing a robust multi-sensor locator suitable for lane-level autonomous driving applications.

    中文摘要 I Abstract III Acknowledgements V Content VI List of Tables XIII List of Figures XVI Chapter 1 Introduction 1 1.1 Background and Literature Review 1 1.2 Motivation, Objective and Contribution 7 1.3 Thesis Outline 9 Chapter 2 Fundamental of Navigation System 12 2.1 Autonomous Vehicle System 12 2.1.1 Autonomous Vehicle Architecture 12 2.1.1.1 Perception Module 13 2.1.1.2 Localization Module 16 2.1.1.3 Planning Module 18 2.1.1.4 Control Module 19 2.1.2 Autoware 21 2.2 Global Navigation Satellite System 23 2.2.1 Overview of GNSS Positioning 23 2.2.2 GNSS Observation Equations 25 2.2.2.1 Pseudorange Measurement 25 2.2.2.2 Carrier Phase Measurement 26 2.2.2.3 Doppler Shift Measurement 27 2.2.3 GNSS Error Source and Performance 28 2.2.3.1 Satellite Orbit Error 28 2.2.3.2 Satellite Clock Bias 29 2.2.3.3 Receiver Clock Bias 30 2.2.3.4 Ionospheric Delay 30 2.2.3.5 Tropospheric Delay 31 2.2.3.6 Multipath Effect and Non-Line of Sight Effect 32 2.2.3.7 Dilution of Precision and User Equivalent Range Error 33 2.2.4 GNSS Positioning Modes 35 2.2.4.1 Standard Point Positioning 36 2.2.4.2 Differential Global Positioning System 37 2.2.4.3 Precise Point Positioning 39 2.2.4.4 Real-Time Kinematic 40 2.3 Inertial Navigation System 41 2.3.1 Definition of Reference Frame 41 2.3.1.1 Inertial Frame 42 2.3.1.2 Earth-Centered, Earth-Fixed Frame 43 2.3.1.3 Navigation Frame 47 2.3.1.4 Body Frame 48 2.3.1.5 Vehicle Frame 49 2.3.1.6 Camera Frame 50 2.3.2 Inertial Sensor Error Source 51 2.3.2.1 Bias Offset and Bias Instability 52 2.3.2.2 Scale Factor Error 53 2.3.2.3 Non-Orthogonality Error 54 2.3.2.4 Misalignment Error 55 2.3.2.5 Random Walk 56 2.3.3 Inertial Sensor Calibration 57 2.3.3.1 Inertial Sensor Measurement Model 57 2.3.3.2 Six-Position Static Test 58 2.3.3.3 Angle Rate Test 59 2.3.3.4 Allan Variance Test 60 2.3.4 Initial Alignment 61 2.3.4.1 Position Initialization 61 2.3.4.2 Velocity Initialization 61 2.3.4.3 Attitude Initialization 62 2.3.5 INS Mechanization 64 Chapter 3 Multi-Sensor Integration Scheme 67 3.1 Kalman Filter in Navigation System 67 3.1.1 Kalman Filter Formulations 68 3.1.1.1 Continuous-Time Formulation 68 3.1.1.2 Discrete-Time Formulation 69 3.1.2 Kalman Filter Variants 69 3.1.2.1 Linear Kalman Filter 70 3.1.2.2 Extended Kalman Filter 70 3.1.3 Kalman Filter Algorithm 71 3.1.3.1 General Procedure 72 3.1.3.2 Prediction Step 73 3.1.3.3 Update Step 74 3.2 Loosely Coupled Scheme 75 3.2.1 Loosely Coupled Error State 76 3.2.2 Loosely Coupled Measurement Model 76 3.3 Tightly Coupled Scheme 77 3.3.1 Tightly Coupled Error State 78 3.3.2 Tightly Coupled Measurement Model 79 3.4 Vehicle Motion Constrains 80 3.4.1 Zero Velocity Update 81 3.4.2 Zero Integrated Heading Rate 82 3.4.3 Non-Holonomic Constrain 83 Chapter 4 Visual-Inertial Odometry 84 4.1 Overview of Visual-Inertial Odometry 84 4.2 Visual Sensor Calibration 85 4.2.1 Camera Intrinsic Parameters 86 4.2.2 Camera-IMU Extrinsic Parameters 87 4.3 Feature Extraction and Tracking 88 4.4 IMU Pre-Integration 90 4.5 Other Visual Localization Approaches 91 4.5.1 Visual Odometry 92 4.5.2 Visual Simultaneous Localization and Mapping 93 4.5.3 Visual-Inertial Odometry 95 Chapter 5 HD Vector Maps Aided Navigation 97 5.1 Overview of HD Maps 97 5.1.1 Point Cloud Maps 98 5.1.2 Vector Maps 99 5.2 YabLoc Framework 100 5.2.1 Image Process Module 101 5.2.1.1 Line Segment Detection 101 5.2.1.2 Graph Construction 103 5.2.1.3 Segment Filter 105 5.2.2 Map Process Module 107 5.2.2.1 Ground Server 107 5.2.2.2 Lanelet2 Decomposition 108 5.2.3 Particle Filter Localization 110 5.2.3.1 General Procedure 110 5.2.3.2 Prediction Step 111 5.2.3.3 Update and Resampling Step 113 5.3 Other Map Matching Approaches 114 5.3.1 Kalman-Based Map Matching 115 5.3.2 Optimization-Based Map Matching 116 5.3.3 Multi-Hypothesis Map Matching 117 Chapter 6 Proposed Methodology 119 6.1 Proposed Hardware System 119 6.1.1 Time Synchronization 119 6.1.2 Hardware Communication 121 6.2 INS/GNSS/Camera/HD Vector Maps LC Scheme 122 6.2.1 Visual-Inertial Odometry Module 125 6.2.2 HD Vector Maps Matching Module 127 6.2.3 Extended Kalman Filter Update 129 6.2.3.1 Coordinate Update 129 6.2.3.2 Velocity Update 131 6.2.3.3 Heading Update 132 Chapter 7 Experiment and Data Acquisitions 134 7.1 Experimental Setup 134 7.1.1 Reference System 135 7.1.2 Testing System 136 7.1.3 Benchmark System 137 7.2 Scenario Description 138 7.2.1 Taichung Shuinan 138 7.2.2 Tainan Shalun 142 Chapter 8 Results and Discussions 148 8.1 Taichung Shuinan 148 8.1.1 Overall Performance 148 8.1.2 Open Sky Area 152 8.1.3 GNSS Challenging Area 157 8.1.4 GNSS Denied Area 162 8.1.5 Lanelet2 Coverage Area 166 8.1.6 Summary of Taichung Shuinan Scenario 172 8.2 Tainan Shalun 173 8.2.1 Overall Performance 174 8.2.2 Open Sky Area 178 8.2.3 GNSS Challenging Area 181 8.2.4 GNSS Denied Area 185 8.2.5 Lanelet2 Coverage 189 8.2.6 Summary of Tainan Shalun Scenario 194 Chapter 9 Conclusion and Future Work 197 9.1 Conclusion 197 9.2 Future Work 199 Reference 200

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