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系統識別號 U0026-1807201922535300
論文名稱(中文) 高精地圖輔助車規整合式定位定向系統於都市區域車道級定位之研究
論文名稱(英文) An Automotive-grade INS/HD Maps/Odometer/GNSS Integration Scheme for Lane-level Navigation Application in Urban Area
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 107
學期 2
出版年 108
研究生(中文) 徐珮晴
研究生(英文) Pei-Ching Hsu
學號 P66064015
學位類別 碩士
語文別 英文
論文頁數 112頁
口試委員 指導教授-江凱偉
口試委員-詹劭勳
口試委員-張智安
中文關鍵字 高精地圖  車規整合式定位定向系統  車道級定位  都市地區 
英文關鍵字 High Definition Maps  Automotive-grade Positioning and Orientation system  Lane-level Vehicular Navigation  Urban Area 
學科別分類
中文摘要 近年來,隨著先進駕駛輔助系統(ADAS)的發展,獲取車輛在道路上的精確位置資訊是首要技術需求。室外動態導航技術中,以包含全球導航衛星系統(GNSS)和慣性導航系統(INS)的整合式定位定向系統(POS)最廣為使用,然而衛星訊號傳遞容易受到如建築物、車輛等障礙物產生遮蔽及反射現象,導致多路徑效應(Multipath)及非直線視距(NLOS)接收,帶有誤差的衛星定位成果更會經由整合影響最後的定位定向效能;此外,此類誤差的產生及變化會根據環境而有所不同,不能像典型的系統誤差透過物理或數學模型消除或減緩。本研究利用高階車載行動測繪系統(MMS)搭載光達之圖資蒐集作業平台建立高精地圖(HD Maps),其為智慧型無人載具所使用的新型態地圖輔助資訊,可提供如車道中心線坐標、高程資訊、道網連接方式及路面曲率等車輛導航參考資訊。首先,本研究提出用於Autoware地圖格式的高精地圖搜索機制以取得地圖資訊;接著使用高精地圖輔助衛星定位優化方案以偵測、剔除或減緩上述錯誤的觀測量及定位解,同時反饋至高程約制系統以提升三維精度;最後,使用延伸型卡爾曼濾波器(EKF)於鬆耦合(LC)架構中建立高精地圖輔助整合式定位定向系統以獲得無縫式導航成果,其中,優化後的衛星定位解可提供更可靠的更新量,從高精地圖萃取的平面坐標、高程和航向資訊亦可加入更新機制以改善整體定位定向效能。本研究的實驗利用低成本衛星接收機獲取多星(GPS/Galileo/QZSS)雙頻(L1/L5)觀測量,透過差分定位(DGNSS)模式及高精地圖輔助衛星定位優化以取得最終衛星定位解,接著使用自動駕駛車輛導航應用的戰術級慣性感測元件(IMU)於所提出的高精地圖輔助整合式定位定向系統。研究成果顯示,使用車規慣性導航、衛星定位、輪速計及高精地圖輔助之整合方案,可達水平方向1.5公尺、垂直方向次公尺級的均方根誤差(RMSE);地圖搜索的車道正確率更超過93%,即系統於超過九成時間可提供正確的地圖資訊作為導航參考。本研究顯示,使用高精地圖作為參考資訊可在都市地區提供更好的車輛導航成果,讓先進駕駛輔助系統更加穩健可行。
英文摘要 Advanced Driver Assistance System (ADAS) technology is increasingly expanding recently, which primary core is to get accurate location information of vehicles. Positioning and Orientation System (POS), which integrates Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), is widely used to complete outdoor navigation mission. However, GNSS positioning is sensitively affected by satellite shielding and signal reflection by buildings or other obstacles in urban canyon, which cause multipath interference and Non-Line-Of-Sight (NLOS) reception. The degraded GNSS would affect the performance of POS after integration process. In addition, the error model of those influences is significantly varied with surroundings and cannot be reduced entirely by physical or mathematical methods. This research utilizes the sophisticated Mobile Mapping System (MMS) to establish the High-Definition Maps (HD Maps), which is the new component in self-driving technology, as reference information for vehicular navigation process. The high precision maps provide information such as the horizontal position of centerline of lane, height value, road network connection, and road curvature, etc. First, the proposed HD maps searching mechanism for selecting the information from Autoware map format. Next, the HD maps aided GNSS optimization scheme can remove or mitigate the error pseudorange measurements and positions due to multipath and NLOS reception, then feedback to navigation process by height constraints system, which can significantly improve the accuracy in both horizontal and vertical direction. Last, the proposed INS/HD maps/Odometer/GNSS integration scheme applies the Extended Kalman filter (EKF) in Loosely-Coupled (LC) architecture to obtain seamless navigation states, which the optimized GNSS can provide reliable update measurements. Moreover, the horizontal position, height, and heading update process derived from HD maps are applied to improve the overall performance. The experiment utilizes automotive-grade GPS/Galileo/QZSS L1/L5 receiver in GNSS-hostile urban environments. Besides, the code-based differential GNSS (DGNSS) positioning is implemented with HD maps aiding information. Then, the tactical-grade Inertial Measurement Unit (IMU), which most HAD vehicular navigation applies recently, is applied in the proposed integration scheme. The results in dense urban area indicate that the INS/HD maps/Odometer/GNSS integration scheme can achieve 1.5 meters in the horizontal direction and less than half a meter in the vertical direction in terms of Root Mean Square Error (RMSE). The lane-level maps searching correct rate is more than 93%, which can provide credible reference most of the time. On account of this research, using HD maps as reference information in vehicular navigation process can provide a better performance, which makes ADAS driving more robust and feasible in urban area.
論文目次 中文摘要 II
Abstract IV
Acknowledgements VI
Contents VIII
List of Tables XI
List of Figures XIII
Chapter 1 Introduction 1
1.1 Background of Vehicular Navigation 1
1.2 Motivation and Objectives 3
1.3 Thesis Outline 5
Chapter 2 INS/GNSS Integration System 6
2.1 Coordinate Frames and Transformations 6
2.1.1 Inertial frame (i-frame) 6
2.1.2 Earth-Centered-Earth-Fixed frame (ECEF, e-frame) 7
2.1.3 Navigation Frame (n-frame) 8
2.1.4 Vehicle Frame (v-frame) and Body Frame (b-frame) 9
2.2 Global Navigation Satellite System (GNSS) 10
2.2.1 GNSS Positioning 11
2.2.2 GNSS Modification 13
2.2.3 Error Source of GNSS and Differential GNSS (DGNSS) 16
2.2.4 Multipath Interference and Non-Line-of-Sight (NLOS) Reception 18
2.3 Inertial Navigation System (INS) 22
2.3.1 Inertial Navigation Equations 23
2.3.2 Inertial Sensor Error Model 26
2.4 INS/GNSS Integration System 28
2.4.1 Kalman Filter (KF) 29
2.4.2 Loosely-Coupled (LC) Integration Scheme 33
2.4.3 Vehicular Motion Constraints and Odometer aided Integration Scheme 34
Chapter 3 Maps aided Navigation Scheme 37
3.1 Existing Maps aided Navigation Scheme 37
3.1.1 Map-Matching Algorithm 37
3.1.2 3D Mapping aided (3DMA) Algorithm 39
3.2 High Definition Maps (HD maps) 40
3.2.1 Introduction of HD maps 40
3.2.2 Establishment of HD maps 42
3.3 HD maps aided Navigation Scheme 49
3.3.1 HD maps Searching Mechanism for Vehicular Navigation 49
3.3.2 HD maps aided GNSS Optimization Scheme 53
3.3.3 INS/HD maps/Odometer/GNSS Integration Scheme 56
Chapter 4 Experiments and Analysis 60
4.1 Experimental Settings and Scenarios Descriptions 60
4.1.1 HD maps for Vehicular Navigation Experiments 60
4.1.2 System Descriptions 64
4.1.3 Experimental Scenarios Descriptions 65
4.2 The Performance of HD maps aided GNSS Optimization Scheme 70
4.2.1 Taiwan CAR Lab 71
4.2.2 Urban Scenario 78
4.3 The Performance of INS/HD maps/Odometer/GNSS Integration Scheme 88
4.3.1 Taiwan CAR Lab 89
4.3.2 Urban Scenario 92
Chapter 5 Conclusions and Future Works 106
5.1 Conclusions 106
5.2 Future Works 107
References 109
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