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研究生： 林冠廷
Lin, Guan-Ting
論文名稱： 結合雷射測距儀實現車輛導航之同步定位與建地圖
Combining Laser Range Finder to Implement SLAM for Vehicle Navigation
指導教授： 莊智清
Juang, Jyh-Ching
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 90
中文關鍵詞： 同步定位與建地圖 、整合式導航技術 、擴展式卡曼濾波器 、雷射測距儀
外文關鍵詞： Simultaneous Localization and Mapping Algorithm, Integration Navigation Technology, Extended Kalman Filter, Laser range finger
相關次數： 點閱：134  下載：4 
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    • 本論文描述多感測器整合系統的定位演算法實現於車輛導航之同步定位與建地圖(Simultaneous Localization and Mapping, SLAM)，以提供車輛在戶外的環境有精確定位資訊，定位準確性在車輛導航方面是非常重要的。目前全球定位系統(Global Positioning System, GPS) 已經發展成熟，且已廣泛的應用在車輛導航上。但是車輛行駛時之定位精確度易受環境影響，如在市區中因大樓遮蔽和多路徑影響，導致訊號中斷。在許多的研究則是運用慣性導航系統(Inertial Navigation System, INS)與GPS整合式導航技術結合擴展式卡曼濾波器(Extended Kalman Filter, EKF)理論，推估載具的位置、速度與姿態，但有可能因為GPS在長時間失去訊號的狀況下，造成推估之誤差累積。為了解決這個問題，本研究則加入雷射測距儀與GPS及IMU之整合，當車輛行駛時，掃描周遭的物體當作參考點，並可得知車輛與參考點之相對距離與角度，藉由實際量測資訊來修正車輛狀態增加導航精度及可靠性，此整合式導航技術則是基於擴展式卡曼濾波器之同步定位與建地圖。
    此研究運用本實驗室之輕型電動車裝載GPS/IMU/Laser range finger，將感測器整合至嵌入式平台繞行校園進行即時接收，將接收資料分析及處理。而此預測車輛狀態基於擴展式卡曼濾波器之同步定位與建地圖演算法將於電腦上驗證。

    This thesis describes the implementation of multi-sensor integrated positioning algorithms for vehicular navigation and simultaneous localization and mapping (SLAM). The positioning algorithm provides precision position information in an outdoor environment. The positioning accuracy for vehicle navigation is very important. Nowadays, Global Positioning System (GPS) has been developed maturely and widely used in vehicle navigation. However, the positioning accuracy of GPS signals of moving vehicle is easily affected by the influence of environment such as shelter and multipath from the building in the city. The signal would be interrupted. In many studies, the integration navigation technology of Inertial Navigation System (INS) and GPS is used to estimate position, velocity and attitude of vehicle based on Extended Kalman Filter (EKF). However, it is possible for GPS signals to lost for a long time causing cumulative error in the estimation. In order to solve this problem, this study adds the laser range finder to integrate the GPS and IMU. The laser range finder scans surrounding object as a reference point (landmark) when the vehicle is driving, and then the relative distance and angle between the vehicle and the landmarks are acquired. The measured information can correct vehicle state, which increase the precision and the reliability of the navigation. This integration navigation technology is developed in SLAM based on EKF.
    This research utilizes the Light-weight Electric Vehicle (LEV) equipped GPS / IMU / Laser range finger. The sensors are integrated into the embedded platform for real-time receiving when the electric vehicle is driving in campus. The received measurement data are analyzed and processed, and then SLAM algorithm based on EKF (EKF-SLAM) is used to estimate vehicle state, which is verified and post processing on the computer.

    摘要 I Abstract II Acknowledgements IV Content VI List of Tables IX List of Figures X List of Abbreviations XIII Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Literature Review 2 1.3 Contributions of the Thesis 5 1.4 Organization 6 Chapter 2. Coordinate System and Modeling of Vehicle and Sensors 7 2.1 Coordinate Systems 7 2.1.1 Earth-Centered Earth-Fixed Coordinate System 7 2.1.2 Local ENU (East-North-Up) Coordinate System 8 2.1.3 Vehicle Coordinate System 9 2.1.4 Sensor Coordinate System 10 2.2 Coordinate Transformation 11 2.2.1 Transformation from Local ENU Coordinate System to Vehicle Coordinate System 15 2.2.2 Transformation from Vehicle Coordinate System to IMU Coordinate System 16 2.2.3 Transformation form Vehicle Coordinate System to Laser Range Finder Coordinate System 17 2.3 Modeling of Laser Range Finder 18 2.4 Modeling of Vehicle Dynamics 19 2.4.1 Kinematic Model 19 Chapter 3. ICP-SLAM and EKF-SLAM Tracking Algorithm 23 3.1 Fundamental of SLAM 23 3.1.1 Extended Kalman Filter Derivation 24 3.1.2 Extended Kalman Filter in SLAM 27 3.2 Map Representation in SLAM 29 3.2.1 Raw Data Map 30 3.2.2 Occupancy Grid Map 30 3.2.3 Feature Based Map 31 3.3 Feature Extraction Method 33 3.3.1 Scanning Segmentation 34 3.3.2 Feature Identification 35 3.3.3 Feature Selection 38 3.4 SLAM Algorithm based on Iterative Closet Point 39 3.4.1 ICP-SLAM Algorithm Implementation 39 3.5 SLAM Algorithm based on Extended Kalman Filter 45 3.5.1 System Models 45 3.5.2 EKF-SLAM Algorithm Implementation 50 Chapter 4. System Implementation and Experiments 60 4.1 Mechatronic System 60 4.1.1 Embedded Real-Time Controller 61 4.1.2 I/O Modules and Sensor Modules 64 4.2 Feature Extraction 66 4.3 Experiment Result 69 4.3.1 Comparison and Analysis of ICP-SLAM and EKF-SLAM 70 4.3.2 State Estimation between EKF Algorithm and EKF-SLAM Algorithm 73 4.4 Human Machine Interface 80 4.4.1 IMU Information 80 4.4.2 Laser Scanning Data Processing 81 4.4.3 GPS Information 82 4.4.4 RTK-GPS and Landmark Information 83 Chapter 5. Conclusions and Future Work 84 5.1 Summary of Results 84 5.2 Future Research 86 Reference 87

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