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系統識別號 U0026-2907202009470100
論文名稱(中文) 整合慣性導航/衛星定位/光達即時製圖與定位技術之多感測器融合策略於無縫導航與製圖之應用
論文名稱(英文) Seamless Navigation and Mapping Using INS/GNSS/LiDAR SLAM Multi-Fusion Schemes
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 108
學期 2
出版年 109
研究生(中文) 蔡光哲
研究生(英文) Guang-Je Tsai
學號 P68031050
學位類別 博士
語文別 英文
論文頁數 164頁
口試委員 指導教授-江凱偉
共同指導教授-朱宏杰
口試委員-卓大靖
口試委員-韓仁毓
口試委員-詹劭勳
口試委員-郭重言
口試委員-Naser El-Sheimy
口試委員-Charles Toth
中文關鍵字 多感測器整合  移動製圖  慣性導航/衛星定位系統  光達  同步定位與地圖構建  無人飛行載具  自動駕駛車 
英文關鍵字 sensor fusion  LiDAR  MMS  INS/GNSS  SLAM  UAV  automated vehicles 
學科別分類
中文摘要 近年來,隨著感測器科技的日新月異,多感測器整合成為未來不管是無人飛機或者自動駕駛車的必要技術之一。不久的將來,我們可以預期到爆炸性的多感測器整合技術發展在各項專業領域上。光學雷達,或簡稱光達,是近年來發展迅速的感測器之一。擁有超過一百公尺以上的測距觀測能力與精準的角度觀測量,使用者可以快速地獲取百公尺之外的環境空間位置資訊,其精確度可以達到公分等級。本論文著重在發展與研究以光達為主的多感測器整合架構,進一步提升傳統移動製圖與同步定位與地圖構建在製圖與定位上的表現。
傳統移動製圖依靠定位定向系統(慣性導航與衛星定位系統的整合),給予製圖感測器絕對且精確的位置與姿態資訊來實現空間資訊的測繪。然而,該方法的精確度卻隨著衛星透空環境而有所變動。在未知的環境或者衛星訊號遮蔽的環境下,無法提供可靠的定位定向資訊,進而影響製圖成果。另一方面,同步定位與地圖構建則是依賴製圖感測器來感知周遭環境的特徵,並利用這些靜態特徵推導出使用者位置與姿態的資訊,可提供準確的相對定位能力並且在偵測到與全域地圖的閉環時,進一步提升準確度。但該方法在空曠或者特徵物稀少的環境下難以發揮優勢,並且長時間累積的誤差難以消除,甚至有演算法發散的風險。
本論文分析傳統移動製圖與同步定位與地圖構建的優缺點,找出兩方法之間的互補特性並且透過慣性導航與衛星定位系統整合光達式的同步定位與地圖構建來驗證。本論文中,提出多項整合策略與方法如下:(1) 階層式的點雲匹配技術與誤差反饋機制於定位定向系統,應用在無人飛行載具進行環境重建;(2) 發展車載動態率定模型,消除在直接地理定位應用中,桿臂與軸角安裝誤差;(3) 提出半緊耦合架構整合定位定向系統與網格式同步定位與地圖構建應於無縫式製圖應用;(4) 透過二維與三維光達資料應用於同步定位與地圖構建技術開發即時導航系統,並透過逐步更新圖資策略與完整性分析克服多路徑效應與長時間衛星斷訊場景。本論文利用高階製圖感測器與傳統控制測量來驗證製圖成效與精確度。透過提出的整合系統,於實驗中的圖資皆採用在全球座標系統中,不再是以往相對座標系統的圖資。於無人飛行載具上,可產製公尺等級的點雲資料,而室內圖資的產製中,即使於長時間衛星遮蔽的情況下,仍可以達到公尺等級。另一方面,本論文採用高階規格的導航系統提拱穩定的參考軌跡來分析與驗證提出的即時導航系統。實驗地點包含台南及高雄的都市峽谷、高架橋周遭與地下停車場等等場景。根據多感測器整合與演算法發展,整體三維成果已可以滿足巷道等級的精確度。
英文摘要 The sensor fusion techniques have become an integral part of future automated vehicles. With advances in sensor technologies, it is predictable for the expected growth of sensor fusion in automotive, military applications. Light Detection and Ranging (LiDAR) is one of the advanced sensor that enables the user to acquire more than 100-meter geospatial measurements with centimeter-level accuracy. The concentration of this thesis is to develop the tightly fusion schemes with inertial navigation system/global navigation satellite system (INS/GNSS)/LiDAR, and enhance the mapping and positioning performance.
Conventional mobile mapping systems (MMS) with INS/GNSS and mapping sensors have been widely developed in recent years. However, current systems and results are still prone to errors, especially in GNSS-denied or multipath environments. On the other hand, simultaneous localization and mapping (SLAM) conducts the positioning and mapping of the environment simultaneously based on the static objects or features surrounded by sensors. Precise relative positioning benefits SLAM to continuously localize itself and can further improve the performance when detecting the loop closure on the map. However, the relative positioning algorithm still suffers from an increase in accumulation error with the traveled distance. Moreover, once the solution of SLAM diverges, it is difficult to re-localize itself on the correct position.
Therefore, this thesis proposes different fusion strategies using INS/GNSS and LiDAR SLAM for unmanned aerial vehicle (UAV) and land vehicle, including following novel approaches: (1) hierarchical point cloud registrations combining generalized-iterative closest point (G-ICP) and direct geo-referencing (DG) to address the local minima problem as well as taking the feedback bias into INS/GNSS; (2) kinematic calibration model to estimate mounting parameters used in DG for land vehicle; (3) semi-tightly coupled integration scheme with INS/GNSS/grid-SLAM to achieve the seamless mapping in long-term GNSS-denied environment; (4) navigation algorithms with refreshing map and integrity assessment strategies using INS/GNSS integrating 2D and 3D SLAM. The mapping performance of proposed approaches are validated using high accurate reference systems and precise traditional control survey. For proposed UAV and land vehicle mapping solutions, the generated point cloud and indoor floor plane achieve the meter-level accuracy even in the long-term GNSS outage. The positioning performance was assessed based on a large number of trajectories collected by different vehicles and different sensors in various scenarios. In general, the 3D positioning error can control under 1.5 meter in various scenarios which means it meet the accuracy requirement of “which lane”. The results show that the proposed fusion schemes to both aerial and land vehicles have the great potential for future mapping and positioning applications.
論文目次 TABLES OF CONTENT
摘要 I
ABSTRACT III
ACKNOWLEDGMENTS V
TABLES OF CONTENT VIII
LIST OF FIGURES XIII
LIST OF TABLES XIX
LIST OF SYMBOLS, ABBREVIATIONS AND NOMENCLATURE XXI
LIST OF REFERENCE FRAMES XXV
1. Inertial Frame XXV
2. Earth Fixed Frame XXV
3. Geodetic Reference Frame XXVI
4. Navigation Frame XXVI
5. Body Frame XXVII
6. Platform Frame XXVII
7. LiDAR Frame XXVIII
8. Occupancy Grid Map Frame XXVIII
Chapter 1. Introduction 1
1.1 Backgrounds 1
1.2 Motivation and Problem Statement 6
1.3 Objectives and Contributions 8
1.4 Thesis Outline 10
Chapter 2. Principles of Multi-Sensor Integrated Navigation Systems 12
2.1 Introduction of Navigation Systems 12
2.1.1 Global Navigation Satellite System 12
2.1.2 Inertial Navigation System 17
2.1.3 Integrated Navigation System 18
2.2 Fundamentals of Inertial Navigation System 19
2.2.1 Inertial Sensor Calibration 19
2.2.2 Error Modelling 20
2.2.3 Initial Alignment 21
2.2.4 Navigation Equations 22
2.3 INS/GNSS Integration Schemes 25
2.3.1 Loosely Coupled Scheme 25
2.3.2 Tightly Coupled Scheme 26
2.4 The Extended Kalman Filter Design for Integrated Navigation System 27
2.4.1 System Model 30
2.4.2 Measurement Models 32
2.5 Optimal Smoothing 35
Chapter 3. LiDAR Direct Geo-Referencing 38
3.1 Fundamentals of Direct Geo-referencing 38
3.2 LiDAR Direct Geo-Referencing Error Sources 39
3.2.1 LiDAR Direct Geo-Referencing Random Errors 40
3.2.2 LiDAR Direct Geo-Referencing Systematic Errors 41
3.3 Kinematic Calibration Model for Land Vehicle MLS 44
Chapter 4. The Proposed Integrated Mapping System for UAV 48
4.1 Development of LiDAR-Based UAV System 49
4.1.1 Mapping Payload Design 49
4.1.2 INS/GNSS/LiDAR Integrated Algorithm for UAV System 50
4.2 New Strategy for Hierarchical Point Cloud Registration 52
Chapter 5. The Proposed Mapping System for Land Vehicles 58
5.1 Seamless INS/GNSS/LiDAR SLAM Integrated Mapping Algorithm 58
5.1.1 Grid-based SLAM 61
5.1.2 INS/GNSS/Grid-Based SLAM Semi-Tightly Coupled Fusion Scheme 63
Chapter 6. The Proposed Integrated Navigation System for Land Vehicles 68
6.1 INS/GNSS/2D LiDAR SLAM Integrated Navigation Algorithm 68
6.1.1 INS/GNSS/Refreshed-SLAM Fusion Algorithm 70
6.1.2 Refreshed Grid-based SLAM 73
6.1.3 Integrity Assessment 75
6.2 INS/GNSS/3D LiDAR SLAM Integrated Navigation Algorithm 77
6.2.1 3D LiDAR SLAM - LiDAR Odometry and Mapping 78
6.2.2 Integration Scheme for INS/GNSS/3D LiDAR SLAM 87
Chapter 7. Field Testing, Results and Discussion 92
7.1 LiDAR-based UAV with Hierarchical Point Cloud Registration 93
7.2 Kinematic Calibration for Land Vehicle MLS Experiment 97
7.3 INS/GNSS/Grid-Based SLAM Semi-Tightly Coupled Fusion Experiment 101
7.3.1 NCKU Underground Parking Lot 102
7.3.2 Hai-an Underground Parking Lot 109
7.4 INS/GNSS/2D LiDAR SLAM Fusion Experiment 118
7.4.1 Scenarios 1: Hai-an Underground Parking Lot 119
7.4.2 Scenarios 2: Tainan Urban and NCKU Campus 123
7.5 INS/GNSS/3D LiDAR SLAM Integrated Navigation Algorithm 135
7.5.1 Kaohsiung Urban and GNSS-hostile Region 135
7.5.2 Kaohsiung Urban and Highway Area 140
Chapter 8. Conclusions and Future Works 146
8.1 Conclusions 146
8.1.1 Kinematic Calibration for Land Vehicle MLS 146
8.1.2 LiDAR-based UAV with Hierarchical Point Cloud Registration 146
8.1.3 Integrated Mapping System for Land Vehicles 147
8.1.4 Integrated Navigation System for Land Vehicles 148
8.2 Future works 149
Appendix – Sensor and Platform Specifications 152
References 157
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