本文關(guān)鍵詞：基于多線激光雷達的道路和障礙物檢測

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• ABSTRACT4-9
• CHAPTER 1 INTRODUCTION9-19
• 1.1 Overview of the Intelligent Vehicle and its Research9-14
• 1.1.1 Intelligent Transportation Systems9-11
• 1.1.2 Intelligent Vehicle11-12
• 1.1.3 Competitive Driverless Car12-14
• 1.2 Common Sensors and Technologies14-17
• 1.3 Mainly Studies, Work and Organization of Research17
• 1.4 Specific Research Contents17-18
• 1.5 Research Arrangements18-19
• CHAPTER 2 MULTIDIMENSIONAL LASER RADAR19-33
• 2.1 Introduction19
• 2.2 General Familiarity with Laser19-27
• 2.2.1 System of Standard Individual Components21-22
• 2.2.2 Ranging Principle of IBEO LUX22-27
• 2.3 Establishment and Transformation between the Coordinate Systems27-31
• 2.3.1 Three-dimensional Coordinate System, Geometric Transformations27-30
• 2.3.2 Establish and Coordinate Transformation30-31
• 2.4 Summary of Chapter 231-33
• CHAPTER 3 EXPERIMENTAL PLATFORM AND VEHICLE TEST33-43
• 3.1 Test Platform Overview33-36
• 3.2 Software System36-37
• 3.2.1 MATLAB36-37
• 3.3 Scan and Data Receiving37-41
• 3.3.1 Analysis of the Scan Data37-38
• 3.3.2 Segmenting the Scan Data38-41
• 3.4 Summary of Chapter 341-43
• CHAPTER 4 ROAD AND OBJECT DETECTION BASED ON MULTI-LAYERLASER RADAR43-65
• 4.1 Introduction43-44
• 4.2 Hough Transform Method44-49
• 4.2.1 Implementation of the Hough Transform44-46
• 4.2.2 The General Algorithm of Hough Method46-49
• 4.3 Theil Sen Estimator49-53
• 4.3.1 Definition49
• 4.3.2 Statistical Properties and Implementation49-53
• 4.4 The Method of Least Squares53-58
• 4.4.1 The Geometry of Ordinary Least Squares54-56
• 4.4.2 The least-squares Method with the Introduction of the Polynomial56-58
• 4.4.3 Advantages and Disadvantages of Least Squares Method58
• 4.5 Bounding Box58-59
• 4.6 The Partitioning of Data into Clusters Using the Clustering Method59-62
• 4.6.1 K-means Algorithm59-60
• 4.6.2 Standard Algorithm60-61
• 4.6.3 Example of Standard Algorithm61-62
• 4.6.4 Problem of the K-means Method62
• 4.7 Summary of Chapter 462-65
• CHAPTER 5 EXPERIMENTAL RESULTS AND ANALYSIS65-89
• 5.1 Introduction65
• 5.2 Object Detection with Hough Line Detection65-72
• 5.3 Object Detection with Theil Sen Estimator72-75
• 5.4 Least Squares Method75-80
• 5.5 Bounding Box80-82
• 5.6 K-means Clustering82-85
• 5.6.1 Specify the Number of Clusters83-85
• 5.7 Comparison of Objects Detection Methods85-88
• 5.8 Summary of Chapter 588-89
• CONCLUSION AND OUTLOOK89-91
• 1. Conclusion89-90
• 2. Outlook90-91
• REFERENCES91-95
• Published Papers during the Academic of Master Degree95-97
• Acknowledgements97