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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃漢邦 | zh_TW |
| dc.contributor.advisor | Han-Pang Huang | en |
| dc.contributor.author | 趙鈺麟 | zh_TW |
| dc.contributor.author | Yu-Lin Zhao | en |
| dc.date.accessioned | 2024-08-07T16:25:23Z | - |
| dc.date.available | 2024-08-08 | - |
| dc.date.copyright | 2024-08-07 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-02 | - |
| dc.identifier.citation | [1] "Ceres Solver: Tutorial & Reference," accessed 12/01 2023. <http://ceres-solver.org/>
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93687 | - |
| dc.description.abstract | 保持機器人建圖和定位的高精度是極具挑戰性的工作，尤其是在複雜多變的環境中，如動態環境、缺少明顯特徵的室內環境以及不平整的室外環境。傳統的單感測器SLAM（同步建圖與定位）方法面臨諸多困難，需要採用更為先進和多樣化的技術來應對這些挑戰。
本論文致力於提升機器人SLAM的強健性、穩定性和準確性。首先，我們建立了搭載深度相機和雷射雷達的智慧型機器人平臺，並分別應用於防疫消毒和協助失智症老人的照護。其次，針對動態環境，我們設計並實現了支援多目標跟蹤的動態環境語義SLAM系統，該系統能夠在未知環境中進行機器人狀態、環境特徵和多目標狀態的聯合估計。隨後，我們對室內外視覺SLAM和雷射雷達SLAM的定位精度、建圖效果和性能進行了綜合評估。最後，我們提出了基於相機與雷射雷達融合的SLAM方法，充分利用兩種儀器的互補優勢。 這些演算法均經過公開資料集或實際場景的測試，結果表明，本論文提出的SLAM系統具有顯著的強健性、穩定性和準確性。 | zh_TW |
| dc.description.abstract | Maintaining high accuracy in robotic mapping and localization is a highly challenging task, especially in complex and dynamic environments, such as those with moving objects, featureless indoor spaces, and uneven outdoor terrains. Traditional single-sensor SLAM (Simultaneous Localization and Mapping) methods face numerous difficulties and require more advanced and diverse technologies to address these challenges.
This research aims to enhance the robustness, stability, and accuracy of SLAM by developing an intelligent robotic platform with depth cameras and 3D LiDAR, and by applying it to both pandemic disinfection and dementia elderly care. The platform implements a specially designed dynamic-environment semantic SLAM system which supports multi-object tracking and concurrent estimation of environmental features, multiple object states in unknown environments, and the robot’s own state. In addition, a comprehensive evaluation is made of the localization accuracy, mapping quality, and performance of indoor and outdoor visual SLAM and LiDAR SLAM. A novel SLAM method is then proposed based on the fusion of camera and LiDAR data, leveraging the complementary advantages of both sensors. These algorithms are tested both on public datasets and in real-world scenarios. The results demonstrate that this new SLAM system exhibits significant robustness, stability, and accuracy. | en |
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| dc.description.provenance | Made available in DSpace on 2024-08-07T16:25:23Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii List of Tables viii List of Figures ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contributions 2 1.3 Organization of Dissertation 3 Chapter 2 Literature Review 6 2.1 Simultaneous Localization and Mapping 6 2.2 Visual SLAM 10 2.3 LiDAR SLAM 13 2.4 Visual LiDAR SLAM 16 2.5 Benchmark and Evaluation 17 Chapter 3 Creating a Robot Platform 20 3.1 Fundamentals of Robotics 20 3.1.1 Hardware 21 3.1.2 Software 22 3.2 Disinfection Robot 23 3.2.1 Hardware 24 3.2.2 Software 30 3.3 Dual-Arm Mobile Robot 36 3.3.1 Hardware 37 3.3.2 Software 39 3.4 Summary 42 Chapter 4 Development of Semantic SLAM for Dynamic Environments 43 4.1 Introduction 43 4.1.1 Object Detection 44 4.1.2 Dynamic SLAM 45 4.1.3 Multiple Object Tracking (MOT) 47 4.2 Methods 49 4.2.1 Semantic SLAM System 49 4.2.2 Development of MOT 51 4.3 Experimental Results 57 4.3.1 SLAM Evaluation 58 4.3.2 MOT Evaluation 60 4.4 Summary 61 Chapter 5 Comparison of Visual SLAM and LiDAR SLAM for Mobile Robots 63 5.1 Introduction 63 5.2 Methods 65 5.2.1 Gmapping 65 5.2.2 ORB-SLAM3 66 5.2.3 SC-LeGO-LOAM 68 5.2.4 Cartographer 70 5.2.5 3D Reconstruction 71 5.3 Performance Evaluation 72 5.3.1 Benchmark 73 5.3.2 Indoor Environment 74 5.3.3 Outdoor Environment 78 5.3.4 Computational Resources 82 5.4 Summary 84 Chapter 6 Multi-Sensor Fusion SLAM 87 6.1 Introduction 87 6.2 System Overview 88 6.3 Methods 89 6.3.1 Alignment Essentials 89 6.3.2 LiDAR Odometry 92 6.3.3 Visual Odometry 95 6.4 Experimental Results 96 6.5 Summary 98 Chapter 7 Conclusions and Future Work 100 7.1 Summary 100 7.1.1 Creating a Robot Platform 100 7.1.2 Development of Semantic SLAM for Dynamic Environments 101 7.1.3 Comparison of Visual SLAM and LiDAR SLAM for Mobile Robots 102 7.1.4 Multi-Sensor Fusion SLAM 102 7.2 Future Work 103 References 105 Biography 113 | - |
| dc.language.iso | en | - |
| dc.subject | 感測器融合 | zh_TW |
| dc.subject | 移動機器人 | zh_TW |
| dc.subject | 多目標跟蹤 | zh_TW |
| dc.subject | SLAM | zh_TW |
| dc.subject | 三維重建 | zh_TW |
| dc.subject | 視覺SLAM | zh_TW |
| dc.subject | 光達SLAM | zh_TW |
| dc.subject | LiDAR SLAM | en |
| dc.subject | SLAM | en |
| dc.subject | 3D reconstruction | en |
| dc.subject | visual SLAM | en |
| dc.subject | multiply object tracking | en |
| dc.subject | mobile robots | en |
| dc.subject | sensor fusion | en |
| dc.title | 實時語義同步定位與地圖建構融合系統開發與在動態環境中移動機器人的應用 | zh_TW |
| dc.title | Real-time Semantic SLAM Fusion for Mobile Robots in Dynamic Environments | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 蔣本基;傅楸善;程登湖;蔡清池;郭重顯 | zh_TW |
| dc.contributor.oralexamcommittee | Pen-Chi Chiang;Chiou-Shann Fuh;Teng-Hu Cheng;Ching-Chih Tsai;Chung-Hsien Kuo | en |
| dc.subject.keyword | SLAM,移動機器人,多目標跟蹤,視覺SLAM,光達SLAM,感測器融合,三維重建, | zh_TW |
| dc.subject.keyword | SLAM,mobile robots,multiply object tracking,visual SLAM,LiDAR SLAM,sensor fusion,3D reconstruction, | en |
| dc.relation.page | 114 | - |
| dc.identifier.doi | 10.6342/NTU202402897 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-08-06 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| 顯示於系所單位： | 機械工程學系 | |
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