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系統識別號 U0026-1407201920415200
論文名稱(中文) 發展適用氣墊船即時定位與製圖技術輔助之GNSS遮蔽區製圖技術
論文名稱(英文) Development of SLAM Aided Integrated Navigation Scheme for Hovercraft Mapping in GNSS-Denied Environments
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) 蔡旻娟
研究生(英文) Min-Chuan Tsai
學號 P66054036
學位類別 碩士
語文別 英文
論文頁數 122頁
口試委員 指導教授-江凱偉
口試委員-詹劭勳
口試委員-陳國華
中文關鍵字 慣性導航系統  即時定位及製圖技術  移動式測繪及製圖系統  氣墊船  地下管線定位及製圖  小波轉換 
英文關鍵字 Hovercraft  Underground mapping and positioning  Initial Navigation System  Simultaneous Localization and Mapping  Mobile Mapping System 
學科別分類
中文摘要 製圖,以空間資訊為根本基礎,透過相機或雷射感測器等設備蒐集環境資訊,進而將所有空間資訊結合並重建環境,以達到空間資訊最大的應用。在製圖前要有能力判斷本身定位,方能確實將各個蒐集點所捕捉到的資訊做最適切的統整與拼接,定位成果的優劣牽涉到製圖精度的好壞,兩者可謂息息相關,本論文的氣墊船製圖亦由定位解的提升來奠定基礎。近年製圖技術多透過慣性導航系統(Inertial Navigation System, INS)以及全球導航定位系統(Global Navigation Satellite System, GNSS)之基礎於室外發展成移動式測繪及製圖系統(Mobile Mapping System, MMS),然而室內環境受頻蔽效應影響未能以上述全球定位系統為基礎做定位,慣性導航系統不受訊號遮蔽等外力影響的特性則得以克服該環境,並承襲室外導航經驗所使用的移動式測繪及製圖系統的整合式概念,搭配即時定位及製圖技術(Simultaneous Localization and Mapping, SLAM)做應用,本論文即是以慣性導航系統輔以即時定位及製圖技術,透過時間同步的整合機制,作為發展氣墊船製圖系統的基礎依據和技術。
有鑑於地下管線圖資缺乏,以及當代社會型態對室內環境的依賴,本研究認為有必要發展適用於室內和地底較險惡環境的製圖系統以延伸導航的應用,除了室內導航的需求,為避免高雄氣爆類似的狀況發生,對於地下管線的監測提供完善製圖系統以及定位需求更是不可或缺,本論文提出以氣墊船為載具、雷射掃描儀搭配慣性感測元件(Inertial Measurement Unit, IMU)的整合式技術,以氣墊船水陸兩地皆能運行的特性來克服地下水道潮濕環境與崎嶇移動面,此狀態下的空間資訊蒐集勢必受震盪影響產生無可避免的雜訊,本論文亦針對此現象提出雜訊分離演算法(denoising algorithm)來因應於整合式導航應用,藉由頻域分析來瞭解氣墊船訊號在移動時產生的特性,進一步去蕪存菁保留真正本體移動的訊號區段,期望提升整合定位解的整體精度,以提供良好定位作為製圖應用的依據。雜訊分離演算法為本研究的具體貢獻,除了一般研究僅使用如ZUPT (Zero Update)和NHC(non-holonomic constraint)等特定的常用約制方法,本論文使用穩定小波轉換(Stationary Wavelet Transform, SWT)以彌補離散小波轉換(Discrete Wavelet Transform, DWT)在執行縮減取樣(downsampling)時導致的平移一致性(translation-invariant)缺失,用以作為進行雜訊分離之根本,針對氣墊船運動模式的分析並在慣性感測元件的原始解予以雜訊分離機制,求取更精確的真實運動訊號,可望延伸到各種整合訊號來分析其特性並達到定位精度更有效的提升。
英文摘要 Indoor navigation as well as pipeline detection has been under attention in recent years due to the device and technology developments with various requirements from urbanization. Thus, GNSS-denied environments are the targets to complete the mapping constructions or positioning applications. In this research, indoor and underground environments are detected and spatial information is acquired by laser scanner as further Simultaneous Localization and Mapping (SLAM) algorithm input. On the other hand, Inertial Navigation System (INS) is aided for improving position accuracy during scanning processes. The payload is mounted on either mobile robot or hovercraft in relative size of the regions based on the concept of Mobile Mapping System (MMS). This research provides the rigid payload for various platforms in related scenarios with INS as well as the laser scanner and the combination of the sensors’ data depends on the synchronization of receiving time by Kalman Filter (KF). The main focus emphasizes the capability of the payload on different platforms for further applications and the ability of the algorithm for accurate positioning so far by individual constraints. This research gives out the results from the robotic and vehicle MMS with the same payload in indoor areas to verify the improved integrated solution. Furthermore, the hovercraft is applied to validate the performance of underground mapping and positioning. Since hovercraft is a relatively distinctive platform for MMS, it would rely on INS results more due to the featureless situations unfavorable for the SLAM algorithm. Thus, the dataset of IMU raw data is additionally transferred to frequency domain for advance analysis. Spectrum analysis is implemented via Fast Fourier Transform (FFT), short-term Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). The research consequently proposes the positional enhancement technique through the integrated solution with the employment of the denoising algorithm to the INS solution. Once the boost of the platform position is received, it can not only strengthen the mapping accuracy by feeding back the positional result but also broaden the usage in a greater scale such as underground positioning.
論文目次 摘要 I
Abstract III
Acknowledgements V
Table of Contents VII
List of Tables XI
List of Figures XII
Glossary of symbols, abbreviation and nomenclature XVI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem statements 3
1.3 Objectives 3
1.4 Thesis outline 5
1.5 Contributions 6
1.5.1 Mapping hovercraft and moveable payload 6
1.5.2 Integrated solution on various platforms 7
1.5.3 Denoised improvement to the raw IMU data 7
Chapter 2 Background and literature review 9
2.1 Coordinate system 9
2.1.1 The Inertial frame (i-frame) 9
2.1.2 The earth frame (e-frame) 10
2.1.3 The navigation frame (n-frame) 11
2.1.4 The body frame (b-frame) 11
2.1.5 The computer frame (c-frame) 12
2.2 The overview of MMS 12
2.3 The trend of MMS in GNSS-degraded scenarios 15
2.3.1 Unmanned Ground Vehicle (UGV) 15
2.3.2 Handheld and backpacking 18
2.3.3 Trolley 20
2.3.4 Unmanned Aerial Vehicle (UAV) 21
2.3.5 The comparison between platforms 22
2.4 Indoor navigation 23
2.5 Underground mapping 25
Chapter 3 Integrated solution for mapping 29
3.1 LiDAR-based SLAM algorithm 29
3.1.1 Occupancy grid map 30
3.1.2 Data preprocessing 30
3.1.3 Map access 31
3.1.4 Scan matching 32
3.1.5 Multi-resolution map representation 33
3.1.6 Motion model 34
3.2 Inertial Navigation System (INS) 34
3.2.1 Transformations 35
3.2.2 Inertial Navigation Dynamic Equations 38
3.2.3 INS mechanization 40
3.2.4 Constraints for drifting problem 41
3.3 Extended Kalman Filter (EKF) 43
Chapter 4 Frequency analysis and denoising algorithm 51
4.1 Spectrum analysis 52
4.1.1 Fourier Transform (FT) 52
4.1.2 Short Term Fourier Transform (STFT) 53
4.1.3 Continuous Wavelet Transform (CWT) 54
4.2 Denoising process 56
4.2.1 Discrete wavelet transform (DWT) 57
4.2.2 The concept and structure for the proposed denoising algorithm 60
Chapter 5 System and experiments 64
5.1 Payload 65
5.1.1 The LiDAR 65
5.1.2 Inertial Measurement Unit (IMU) 66
5.2 Platforms 67
5.2.1 TurtleBot 67
5.2.2 Vehicle 68
5.2.3 Hovercraft 68
5.2.4 The characteristics between platforms 69
5.3 The structure of proposed MMS 70
5.4 Experimental fields 72
Chapter 6 Results analysis and comparisons 76
6.1 Evaluations of the integrated method on common platforms 76
6.2 Frequency analysis 77
6.2.1 FFT 78
6.2.2 STFT 87
6.2.3 CWT 94
6.2.4 Comparisons of frequency analysis for different platforms 100
6.3 Spectrum analysis of denoising algorithm 102
6.4 Denoised performance 105
6.5 Comparisons of trajectories 107
6.6 Mapping results from existing SLAM algorithm 110
Chapter 7 Conclusions and future works 114
Reference 116
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