臺灣博碩士論文加值系統

近幾年，機器人相關領域漸漸受到矚目。SLAM這個領域已經被研究一段時間，是一項機器人完成高難度任務所需的基礎。然而，感測器融合的重要性在近幾年才開始被研究。
光達與慣性感測器融合之定位與建圖系統在這篇論文中被提出，其中也包含回環偵測及全局位姿優化。光達與慣性感測器被使用來互相補足對方的缺點，達到更好的位置與姿態估測。在SLAM系統中，估測出的位姿及地圖提供機器人環境資訊，使其實現搜索式路徑規劃。在同時考慮環境中障礙物與自身的運動限制下，搜索式路徑規劃可以產生出一條平滑且最短時間的軌跡。
最後，這個光達與慣性感測器系統在KITTI資料集中測是，以評估其方法之精確度。同時也在室內環境中，在無人機上實現搜索式路徑規劃，達到實時地產生一條局部最佳的路徑。

In recent years, the field of robotics has attracted lots of attention. For the robot to perform high level tasks, SLAM becomes a fundamental technique to build on. The SLAM problem has been well studied for a period of time, but the importance of sensor fusion to complement each other was just investigated in the past ten years.
In this work, a tightly-coupled lidar-inertial SLAM system is developed with loop-closure and pose graph optimization. The estimate pose and map from the SLAM system provide the knowledge of the environment for the search-based motion planning method. By taking the obstacles in the surrounding and the motion constraints into consideration, a smooth and minimum-time trajectory is generated.
Finally, the lidar-inertial SLAM system is evaluated in KITTI dataset, and an indoor flight experiment has proven the capability of generating a locally-optimal trajectory in real-time.

Abstract i
摘要 ii
Acknowledgements iii
Contents iv
List of Figures vii
List of Tables viii
Notations ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Statement 2
1.3 Contribution 2
Chapter 2 Background and Related Works 3
2.1 Lidar Ego Motion Estimation 3
2.2 Loop-Closure and Pose-Graph Optimization 4
2.3 Motion Planning 5
2.4 System Overview 6
Chapter 3 Front-End: Lidar-Inertial SLAM 7
3.1 Estimator Initialization 7
3.1.1 IMU Pre-Integration 8
3.1.2 Lidar Feature Extraction and Frame-to-Frame Odometry 11
3.1.3 Gyroscope Bias Estimation 11
3.1.4 Gravity and Velocity Estimation 12
3.1.5 Gravity Estimation with High Speed Motion 13
3.1.6 Completing Initialization 13
3.2 Tightly-Coupled Lidar-Inertial SLAM 14
3.2.1 Formulation 14
3.2.2 IMU Measurement Residual 17
3.2.3 Lidar Measurement Residual 17
3.2.4 Marginalization 19
3.2.5 Mapping for The Lidar Point Cloud 21
Chapter 4 Back-End: Loop-Closure and Pose Graph Optimization 22
4.1 Loop-Closure and Relocalization 23
4.1.1 Keyframe Selection 23
4.1.2 Ground Segmentation 24
4.1.3 Loop-Closure 25
4.1.4 Tightly-Coupled Relocalization 27
4.2 Global Pose Graph Optimization 28
4.2.1 Adding Keyframes into Pose Graph 28
4.2.2 4-DOF Pose Graph Optimization 30
4.2.3 Global Consistency Maintenance 31
Chapter 5 Search-Based Motion Planning 32
5.1 Optimal Trajectory Planning 32
5.2 Real-Time Heuristic Search 35
5.3 Collision Checking for Trajectory 37
Chapter 6 Experiments 39
6.1 Front-End Performance 39
6.2 Full Closed Loop Performance 40
6.2.1 Evaluate with KITTI Evaluation Tool 40
6.2.2 Evaluate with EVO 46
6.3 Indoor Planning Test 48
6.3.1 Comparison with Global Planning 48
6.3.2 Re-planning with Unknown Environment 50
6.4 Time Consumption 53
Chapter 7 Conclusion and Future Work 54
7.1 Conclusion 54
7.2 Future Work 55
Reference 56
Appendix 58

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