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研究生：	黃吉歆 Huang, Chi-Hsin
論文名稱：	AIO-NAV 之設計與驗證：以慣性導航為核心，整合全球導航衛星系統、光達與高精地圖輔助之一體機導航框架 Design and Validation of AIO-NAV: An INS-Centric All-In-One Navigation Framework Integrating GNSS, LiDAR, and HD Map Aiding
指導教授：	江凱偉 Chiang, Kai-Wei
學位類別：	博士 Doctor
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：	244
中文關鍵詞：	慣性導航、多感測器融合、光達里程計、高精地圖輔助、自主導航
外文關鍵詞：	Inertial navigation, Multi-sensor fusion, LiDAR odometry, HD map aiding, Autonomous navigation
相關次數：	點閱：94 下載：0
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自主系統廣泛應用於陸上載具、無人飛行器、海洋平台與行動機器人等領域，均需可靠的地球參考位置、速度與姿態估計。全球導航衛星系統（GNSS）是戶外環境中最直接的位置與速度來源；以自動駕駛為例，通常需達次公尺級導航精度。開闊地條件下 GNSS 往往可滿足此需求，惟於 GNSS 受阻環境中訊號遮蔽與多路徑削弱可靠性，於 GNSS 拒止環境中則無法使用。單一感測器無法在所有條件下維持所需精度，多感測器融合因而被視為實務上的可行策略。然而實務上導航常作為應用專屬之子系統開發，而非可重複使用的通用導航器，致導航技術大致沿大地導航與機器人同時定位與建圖（SLAM）兩條路徑發展，感測配置、狀態表示與輔助假設互不相容。多數以感知為核心的系統亦仰賴連續感知輸出與運算密集的優化方案，難以在嵌入式硬體上即時運作。

本論文延續作者先前期以相機為主之 All-In-One Navigation（AIO-NAV）研究，提出並實驗驗證以光達（LiDAR）為感測配置之一體機導航框架。其一體機設計目標為：全感測器——慣性測量單元（IMU）、GNSS、光達與相機同置於單一緊湊剛性平台；全情境——透過模組化輔助支援開闊地、GNSS 受阻與 GNSS 拒止等運行條件；全系統——提供可跨自主系統移轉、不依賴輪速計與非完整性約束之導航優先核心引擎，並以陸上自動駕駛車為代表驗證、可延伸至其他自主系統。以遞迴濾波為基礎之慣性導航（INS）機械編排維持地球參考導航狀態，GNSS 與不變光達里程計輔助（VUPT）於可用時提供更新；即時直接地理定位（DG）與正規分布變換（NDT）地圖匹配於同一慣性導航核心導航器中連結大地導航與地圖定位，光達專責里程計與地圖輔助，相機則移至應用層。

此一體機導航框架於 NVIDIA Jetson AGX Orin 上以機器人作業系統 2（ROS 2）模組化實作，地圖輔助與主導航濾波器解耦，並以另一個誤差狀態卡爾曼濾波器（ESKF）估計地圖-導航偏差。陸上車載實驗涵蓋 GNSS 多路徑、持續拒止及部分與完整 NDT 地圖覆蓋情境。結果顯示：VUPT 可取代輪速計與非完整性約束；GNSS 拒止下核心導航維持可航性，但僅靠里程計時位置誤差常達公尺級。部分地圖輔助將地下拒止區間三維均方根誤差（RMSE）由 1.105 m 降至 0.147 m；全程地圖輔助在無即時動態定位（RTK）GNSS、僅公尺級單點定位（SPP）下，於整段市區路線維持次公尺三維 RMSE（0.401 m）。AIO-NAV-HDMAP（啟用地圖輔助延伸模組之 AIO-NAV）於 Jetson AGX Orin 上 CPU 負荷低於 25%，留有資源供應用層模組使用。

Reliable Earth-referenced position, velocity, and attitude estimation is required by many autonomous systems, including land vehicles, unmanned aircraft, marine platforms, and mobile robots. Global navigation satellite system (GNSS) is the most direct outdoor position and velocity source; autonomous driving typically needs sub-meter accuracy. GNSS often meets this need in open sky, but multipath and blockage degrade it in GNSS-challenging conditions, and it is unavailable in GNSS-denied environments. No single sensor sustains the required accuracy across all conditions, so multi-sensor fusion is the practical approach. In practice, navigation is often built as an application-specific subsystem rather than a reusable navigator, and the field has largely split into geodetic navigation and robotics simultaneous localization and mapping (SLAM) with incompatible sensor suites, states, and aiding assumptions. Perception-centric systems further rely on continuous perception outputs and heavy graph optimization, which are hard to sustain on embedded hardware in real time.

Building on the author's prior camera-based All-In-One Navigation (AIO-NAV) work, this dissertation develops and validates a light detection and ranging (LiDAR)-based configuration for the all-in-one goal: all sensors—inertial measurement unit (IMU), GNSS, LiDAR, and an optional camera on one compact rigid platform; all scenarios—modular aiding from open-sky through GNSS-challenging to GNSS-denied operation; and all systems—a navigator-first core transferable across autonomous systems without wheel odometry or non-holonomic constraints, validated on a land vehicle and designed for extension to other platforms. A filter-based inertial navigation system (INS)-centric architecture propagates the Earth-referenced state at high rate, while GNSS and invariant LiDAR odometry through velocity update (VUPT) provide aiding when available. Real-time direct georeferencing (DG) and optional normal distributions transform (NDT) map registration link geodetic navigation and map localization in one INS-centric navigator; LiDAR supports odometry and map aiding, and the camera is moved to an optional application layer.

The framework is implemented as a modular Robot Operating System 2 (ROS 2) pipeline on an NVIDIA Jetson AGX Orin, with map aiding decoupled from the main navigation filter and a separate error-state Kalman filter (ESKF) estimating the map-navigation bias. Land-vehicle experiments cover multipath GNSS, sustained GNSS denial, and partial and full NDT map coverage. Results show that VUPT can replace wheel odometry and non-holonomic constraints; under GNSS denial the core navigator remains operable, but position error typically stays near or above the meter level without map aiding. With partial map coverage during an underground outage, 3D root mean square error (RMSE) decreases from 1.105 m to 0.147 m; with full-map coverage and meter-level single-point positioning (SPP) GNSS, map aiding maintains sub-meter 3D RMSE (0.401 m) over an urban route without real-time kinematic (RTK) corrections. AIO-NAV with map aiding enabled (AIO-NAV-HDMAP) uses less than 25% CPU on the Jetson AGX Orin, leaving resources for application-layer modules.

摘要 i
Abstract iii
Acknowledgments v
Table of Contents vi
List of Tables x
List of Figures xi
Nomenclature xiii
Chapter 1. Introduction 1
1. Background 1
2. Motivation and problem statement 7
3. Scope and objectives 9
4. Contributions 11
5. Dissertation outline 12
Chapter 2. Fundamentals of Inertial Navigation System 13
1. Rigid Transformation and Attitude Expression 13
1.1. DCM in Terms of Rotation Vector 15
1.2. Quaternion in Terms of Rotation Vector 16
1.3. DCM in Terms of Euler Angles 18
2. Reference Frame 19
2.1. Earth-Centered Frames 19
2.1.1. Earth-Fixed Frame (e-frame) 20
2.1.2. Inertial Frame (i-frame) 21
2.2. Navigation Frame (n-frame) 22
2.3. Vehicle Frame (v-frame) 24
2.4. Body Frame (b-frame) 25
3. INS Mechanization 27
3.1. Kinematics Modeling in the i-frame 27
3.2. Apparent Accelerations in the Rotating e-frame 29
3.3. INS Mechanization in the n-frame 32
4. Discrete INS Updates 35
4.1. IMU increments 35
4.2. Earth model and midpoint states 36
4.3. Velocity update 37
4.4. Position update 38
4.5. Attitude update 39
5. Normal Gravity Model 40
6. Psi-Angle Error Model 41
7. Residual Sensor Error Model 45
Chapter 3. Introduction to Sensor Fusion 48
1. Kalman Filter-based Sensor Fusion 48
1.1. Weighted Least Squares Estimator 48
1.2. Kalman Filter 49
1.3. Linearized Weighted Least Squares Estimator 52
1.4. Error-State Kalman Filter 54
1.4.1. System Predict 57
1.4.2. Measurement Update 59
1.4.3. Error Feedback 60
2. General Aiding Sources 63
2.1. GNSS Measurement Update 63
2.1.1. Position Update 63
2.1.2. Velocity Update 66
2.1.3. Time Synchronization and Delay Compensation 67
2.2. Zero Velocity Update 73
2.3. Zero Integrated Heading Rate Update 75
3. Vehicle Frame Aiding 78
3.1. Odometer Scale Error Modeling 80
3.2. Mounting Angle Error Modeling 81
3.3. Linearized v-frame measurement model 82
4. LiDAR Odometry / LiDAR-Inertial Odometry (LO/LIO) 82
5. High Definition Map Aiding 85
6. Optimization-based Sensor Fusion 87
Chapter 4. Methodology 89
1. INS-Centric Navigation Framework 89
1.1. ROS 2 Environment 90
1.1.1. Core Navigation Node 90
1.1.2. LiDAR Odometry Node 92
1.1.3. NDT Localization Node 93
1.1.4. Map Aiding Node 94
2. Core Navigation Engine 96
2.1. Initial Alignment 97
2.1.1. Static coarse alignment 97
2.1.2. In-motion GNSS heading alignment 99
2.1.3. Fine alignment 101
2.2. Invariant Odometry Aiding 102
2.2.1. Measurement Model of VUPT 104
2.2.2. Observability of VUPT 105
2.3. Sensor Calibration 111
2.4. Time Synchronization and Delay Compensation 115
3. Map Aiding Extension 116
3.1. NDT Map 117
3.1.1. Offline Map Builder 117
3.1.2. Map File Format 118
3.1.3. Spatial Tiling 119
3.1.4. Coordinate System 120
3.1.5. Map Compression 121
3.2. NDT Localization 122
3.2.1. Navigation Pose Synchronization and Initial Guess 123
3.2.2. Tile Selection and Map Loading 125
3.2.3. Direct Georeferencing 127
3.2.4. NDT Registration 129
3.2.5. Map-Navigation Bias Estimation 130
4. Comprehension Module 134
4.1. Role in the Application Layer 134
4.2. Open-Vocabulary Perception Stack 135
4.3. Prototype Example and Extensions 136
Chapter 5. Experiments 138
1. Experimental Setup 138
1.1. AIO-NAV Prototype 138
1.2. Sensor Configuration 140
1.3. Data Collection and Evaluation 141
1.4. Baseline and Benchmark Systems 142
1.5. Reference System 143
2. Core Navigation Experiment Design 146
2.1. GNSS-Challenging Scenario 146
2.2. GNSS-Outage Scenario 147
2.3. Temporary VUPT Interruption 148
2.4. LiDAR-Challenging Scenario 149
3. Map-Aiding Experiment Design 150
3.1. Partial-Map Scenario 150
3.2. Full-Map Scenario 152
Chapter 6. Result and Discussion 153
1. Core Navigation Results 153
1.1. GNSS-Challenging Scenario 153
1.1.1. Motivation 155
1.1.2. Marked Multipath-Rich Segment 155
1.1.3. Overall Route Performance 159
1.2. GNSS-Outage Scenario 163
1.2.1. Motivation 163
1.2.2. GNSS-Outage Interval 163
1.3. Temporary VUPT Interruption 169
1.4. LiDAR-Challenging Scenario 171
1.5. Discussion 174
2. Map-Aiding Results 176
2.1. Partial-Map Scenario 176
2.1.1. Motivation 178
2.1.2. GNSS-Outage Interval: NCKU Library 178
2.1.3. GNSS-Outage Interval: Hai'an Road 183
2.2. Full-Map Scenario 191
2.2.1. Motivation 192
2.2.2. GNSS-Outage Interval 193
2.2.3. Indoor-to-Outdoor Transition 197
2.2.4. Overall Route Performance 199
2.3. Discussion 201
3. Computational Cost and Real-Time Performance 203
3.1. Operating Frequencies 203
3.2. LiDAR to Keypoint (KISS-ICP) 204
3.3. NDT Latency and Real-Time Map Aiding 204
3.4. CPU and GPU Load 207
3.5. Discussion 208
Chapter 7. Conclusion and Future Work 209
1. Summary 209
2. Contributions 209
3. Main Findings 211
4. Limitations 212
5. Future Work 213
References 214
Appendix A. Attitude Conversion Formulas 220
A.1. DCM in Terms of Quaternion 220
A.2. Quaternion in Terms of DCM 220
A.3. Euler Angles in Terms of DCM 222
A.4. Quaternion in Terms of Euler Angles 222
Appendix B. Geodetic Coordinate Conversion 224
B.1. WGS84 Ellipsoid Parameters 224
B.2. Geodetic to Earth-Fixed Cartesian 225
Appendix C. Error-State Transition Matrix 226
C.1. Navigation Block 226
C.2. Sensor Error States 227
                                    

[1] Pratvadi Agarwal, Wolfram Burgard, and Cyrill Stachniss. Geodetic approaches to mapping and relationship to graph-based SLAM. IEEE Robotics & Automation Magazine, 21(3):63–80, 2014.
[2] Paul J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256, 1992.
[3] Harold D. Black. A passive system for determining the attitude of a satellite. AIAA Journal, 2(7):1350–1351, 1964.
[4] Nurlan Boguspayev, Daulet Akhmedov, Almat Raskaliyev, Alexandr Kim, and Anna Sukhenko. A comprehensive review of GNSS/INS integration techniques for land and air vehicle applications. Applied Sciences, 13(8):4819, 2023.
[5] Tim Caselitz, Bastian Steder, Michael Ruhnke, and Wolfram Burgard. Monocular camera localization in 3D LiDAR maps. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1926–1931, 2016.
[6] Chang Chen, Hua Zhu, Menggang Li, and Shaoze You. A review of visual-inertial simultaneous localization and mapping from filtering-based and optimization-based perspectives. Robotics, 7(3):45, 2018.
[7] Kenny Chen, Ryan Nemiroff, and Brett T. Lopez. Direct lidar-inertial odometry: Lightweight lio with continuous-time motion correction. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3983–3989, 2023.
[8] Qijin Chen, Huan Lin, Jian Kuang, Yarong Luo, and Xiaoji Niu. Rapid initial heading alignment for mems land vehicular gnss/ins navigation system. IEEE Sensors Journal, 23(7):7656–7666, 2023.
[9] Tianheng Cheng, Lin Song, Yixiao Ge, Wenyu Liu, Xinggang Wang, and Ying Shan. YOLO-World: Real-time open-vocabulary object detection. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16901–16911, 2024.
[10] Kai-Wei Chiang, Chi-Hsin Huang, Mengchi Ai, Ting-Chun Wu, Pei-Ru Lu, Chin-Chia Hsu, Meng-Lun Tsai, Mohamed Elhabiby, and Naser El-Sheimy. AIO-NAV: INS-centric all-in-one navigation framework integrating mono camera, GNSS, and HD map for large-scale autonomous driving. IEEE Open Journal of Intelligent Transportation Systems, 7:1209–1222, 2026.
[11] Kai-Wei Chiang, Surachet Srinara, Yu-Ting Chiu, Syun Tsai, Meng-Lun Tsai, Chalermchon Satirapod, Naser El-Sheimy, and Mengchi Ai. Creation and verification of high-definition point cloud maps for autonomous vehicle navigation. IEEE Internet of Things Journal, 11(23):37582–37598, 2024.
[12] Kai-Wei Chiang, Surachet Srinara, Syun Tsai, Cheng-Xian Lin, and Meng-Lun Tsai. High-definition-map-based LiDAR localization through dynamic time-synchronized normal distribution transform scan matching. IEEE Transactions on Vehicular Technology, 72(6):7011–7023, 2023.
[13] Minjun Choi, Junhyeong Ryu, Yongseok Son, Sungrae Cho, and Jeongyeup Paek. Lidar-based localization for autonomous vehicles - survey and recent trends. In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), pages 456–460. IEEE, 2024.
[14] Gamal Elghazaly, Raphaël Frank, Scott Harvey, and Stefan Safko. High-definition maps: Comprehensive survey, challenges, and future perspectives. IEEE Open Journal of Intelligent Transportation Systems, 4:527–550, 2023.
[15] Mostafa Elhashash, Hessah Albanwan, and Rongjun Qin. A review of mobile mapping systems: From sensors to applications. Sensors, 22(11):4262, 2022.
[16] Mohamed Elsayed, Eslam Mounier, Emma Dawson, and Aboelmagd Noureldin. Lidar-to-map registration: Comparative analysis of mechanical and solid-state lidar technologies across icp and ndt algorithms. In 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), pages 890–899, Salt Lake City, UT, USA, 2025.
[17] Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza. Imu preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015.
[18] Thomas D. Gillespie. Fundamentals of Vehicle Dynamics. SAE International, Warrendale, PA, USA, 2021.
[19] Paul Groves. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition. Artech House, Norwood, MA, USA, 2013.
[20] Jianjun Gui, Dongbing Gu, Sen Wang, and Huosheng Hu. A review of visual inertial odometry from filtering and optimisation perspectives. Advanced Robotics, 29(20):1289–1301, 2015.
[21] Guoquan Huang. Visual-inertial navigation: A concise review. In Proc. International Conference on Robotics and Automation (ICRA), pages 9572–9582, 2019.
[22] Yuze Jiang, Ehsan Javanmardi, Manabu Tsukada, and Hiroshi Esaki. Enhancing autonomous vehicle localization through cooperative LiDAR and smart infrastructure integration. IEEE Open Journal of Intelligent Transportation Systems, 7:551–564, 2026.
[23] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pages 4015–4026, 2023.
[24] Kenji Koide. ndt_omp: Multi-threaded and SSE-friendly NDT algorithm. GitHub repository, 2021.
[25] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10965–10975, 2022.
[26] Cheng-Yueh Liu. The performance evaluation of a real-time low-cost mems ins/gps integrated navigator with aiding from zupt/zihr and non-holonomic constraint for land applications. In Proc. IEEE/ION Position, Location and Navigation Symposium (PLANS), pages 463–470, Myrtle Beach, SC, USA, 2012.
[27] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, and Lei Zhang. Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection. In Proc. European Conference on Computer Vision (ECCV), pages 38–55, 2024.
[28] Martin Magnusson, Andreas Nüchter, Christopher Lorken, Achim J. Lilienthal, and Joachim Hertzberg. Evaluation of 3d registration reliability and speed - a comparison of icp and ndt. In 2009 IEEE International Conference on Robotics and Automation, pages 3907–3912, 2009.
[29] Peter S. Maybeck. Stochastic Models, Estimation, and Control, volume 3. Academic Press, New York, NY, USA, 1982.
[30] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Basit Mustafa, Moustapha Cisse, Tobias Tieleman, Jasper Uijlings, Mario Lučić, and Xiaohua Zhai. Simple open-vocabulary object detection. In Proc. European Conference on Computer Vision (ECCV), pages 728–755, 2022.
[31] Richard M. Murray, Zexiang Li, and S. Shankar Sastry. A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton, FL, USA, 2017.
[32] National Land Surveying and Mapping Center. TWD97 / TM2 zone 121. EPSG Geodetic Parameter Dataset, EPSG:3826, 2008. Revision date 2008-08-11; Transverse Mercator, false easting 250000 m.
[33] Xiaoji Niu, Hailiang Tang, Tisheng Zhang, Jing Fan, and Jingnan Liu. Ic-gvins: A robust, real-time, ins-centric gnss-visual-inertial navigation system. IEEE Robotics and Automation Letters, 8(1):216–223, 2023.
[34] Aboelmagd Noureldin, Tashfeen Karamat, and Jacques Georgy. Fundamentals of Inertial Navigation, Satellite-Based Positioning and Their Integration. Springer-Verlag, Heidelberg, Germany, 2013.
[35] NVIDIA. Nanoowl: Real-time open-vocabulary object detection with TensorRT on jetson. https://github.com/NVIDIA-AI-IOT/nanoowl, 2023. Accessed 2026-06-20.
[36] NVIDIA. Nanosam: A distilled SAM model for real-time segmentation with TensorRT on jetson. https://github.com/NVIDIA-AI-IOT/nanosam, 2023. Accessed 2026-06-20.
[37] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Proc. International Conference on Machine Learning (ICML), pages 8748–8763. PMLR, 2021.
[38] Tyler G. R. Reid, Sarah E. Houts, Robert Cammarata, Graham Mills, Siddharth Agarwal, Ankit Vora, and Gaurav Pandey. Localization requirements for autonomous vehicles. SAE International Journal of Connected and Automated Vehicles, 2(3):173–190, 2019.
[39] Paul G. Savage. Strapdown Analytics, volume 2. Strapdown Associates, Maple Plain, MN, USA, 2000.
[40] B. M. Scherzinger. Inertial navigator error models for large heading uncertainty. In Proc. IEEE/ION Position, Location and Navigation Symposium (PLANS), pages 477–484, 1996.
[41] P. K. Seidelmann, B. A. Archinal, M. F. A'Hearn, A. Conrad, G. J. Consolmagno, D. Hestroffer, J. L. Hilton, G. A. Krasinsky, G. Neumann, J. Oberst, et al. Report of the IAU/IAG working group on cartographic coordinates and rotational elements: 2006. Celestial Mechanics and Dynamical Astronomy, 98(3):155–180, 2007.
[42] Tixiao Shan and Brendan Englot. Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4758–4765, 2018.
[43] Tixiao Shan, Brendan Englot, Drew Meyers, Wei Wang, Carlo Ratti, and Daniela Rus. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5135–5142, 2020.
[44] Xiaoyu Shan, Adnane Cabani, and Houcine Chafouk. A survey of vehicle localization: Performance analysis and challenges. IEEE Access, 11:107085–107107, 2023.
[45] Stanley W. Shepperd. Quaternion from rotation matrix. Journal of Guidance and Control, 1(3):223–224, 1978.
[46] Eui-Hyun Shin. Estimation Techniques for Low-Cost Inertial Navigation. PhD thesis, University of Calgary, Calgary, Canada, 2005.
[47] Malcolm D. Shuster. A survey of attitude representations. The Journal of the Astronautical Sciences, 41(4):439–517, 1993.
[48] Hailiang Tang, Tisheng Zhang, Xiaoji Niu, Jing Fan, and Jingnan Liu. Impact of the earth rotation compensation on mems-imu preintegration of factor graph optimization. IEEE Sensors Journal, 22(17):17194–17204, 2022.
[49] United States. Defense Mapping Agency. Department of Defense World Geodetic System 1984: Its Definition and Relationships with Local Geodetic Systems, volume 8350 of DMA Technical Report. Defense Mapping Agency, Washington, DC, USA, 1987.
[50] Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch, Louis Wiesmann, Jens Behley, and Cyrill Stachniss. Kiss-icp: In defense of point-to-point icp–simple, accurate, and robust registration if done the right way. IEEE Robotics and Automation Letters, 8(2):1029–1036, 2023.
[51] Liqiang Wang, Xiaoji Niu, Tisheng Zhang, Hailiang Tang, and Qijin Chen. Accuracy and robustness of odo/nhc measurement models for wheeled robot positioning. Measurement, 201:111720, 2022.
[52] Wei Xu, Yixi Cai, Dongjiao He, Jiarong Lin, and Fu Zhang. Fast-lio2: Fast direct lidar-inertial odometry. IEEE Transactions on Robotics, 38(4):2053–2073, 2022.
[53] Wei Xu and Fu Zhang. Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter. IEEE Robotics and Automation Letters, 6(2):3317–3324, 2021.
[54] Kento Yabuuchi, David Robert Wong, Takeshi Ishita, Yuki Kitsukawa, and Shinpei Kato. Visual localization for autonomous driving using pre-built point cloud maps. In Proc. IEEE Intelligent Vehicles Symposium (IV), pages 913–919, 2021.
[55] Man Yu, Keyang Gong, Weihua Zhao, and Rui Liu. Lidar and imu tightly coupled localization system based on ground constraint in flat scenario. IEEE Open Journal of Intelligent Transportation Systems, 5:296–306, 2024.
[56] Pei Yu, Wei Wei, Jing Li, Fang Wang, Lili Zhang, and Zengqiang Chen. An improved autonomous inertial-based integrated navigation scheme based on vehicle motion recognition. IEEE Access, 11:104806–104816, 2023.
[57] Ji Zhang and Sanjiv Singh. Loam: Lidar odometry and mapping in real-time. In Robotics: Science and Systems Conference, 2014.
[58] Yuxiao Zhang, Alexander Carballo, Hanting Yang, and Kazuya Takeda. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 196:146–177, 2023.
[59] Zaixing Zhang, Xiaoji Niu, Hailiang Tang, Qijin Chen, and Tisheng Zhang. Gnss/ins/odo/wheel angle integrated navigation algorithm for an all-wheel steering robot. Measurement Science and Technology, 32(11):115122, aug 2021.
[60] Zeyuan Zhao, Jichao Jiao, Ning Li, and Min Pang. Liop: Tightly coupled lidar-inertial odometry and prior information system for long-term localization*. In 2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pages 1–6, 2024.
[61] Shuran Zheng, Jinling Wang, Chris Rizos, Weidong Ding, and Ahmed El-Mowafy. Simultaneous localization and mapping (SLAM) for autonomous driving: Concept and analysis. Remote Sensing, 15(4):1156, 2023.
[62] Guangzhao Zhou, Haihui Yuan, Shiqiang Zhu, Zhiyong Huang, Yanfu Fan, Xinliang Zhong, Ruilong Du, and Jianjun Gu. Visual localization in a prior 3D LiDAR map combining points and lines. In Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1198–1203, 2021.
[63] Chaoyang Zhu and Long Chen. A survey on open-vocabulary detection and segmentation: Past, present, and future. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):8954–8975, 2024.
[64] Jun Zhu, Hongyi Li, and Tao Zhang. Camera, lidar, and imu based multi-sensor fusion slam: A survey. Tsinghua Science and Technology, 29(2):415–429, 2024.

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