系統識別號 U0026-0207201914045000
論文名稱(中文) 多車地圖合併之即時定位與地圖構建
論文名稱(英文) Simultaneous localization and mapping via Multi-vehicle Map Merging
校院名稱 成功大學
系所名稱(中) 航空太空工程學系
系所名稱(英) Department of Aeronautics & Astronautics
學年度 107
學期 2
出版年 108
研究生(中文) 徐仕旻
研究生(英文) Shih-Min Hsu
學號 P46061267
學位類別 碩士
語文別 英文
論文頁數 62頁
口試委員 指導教授-譚俊豪
中文關鍵字 機器人作業系統  自駕車  即時定位與地圖  自主探索  導航  模型預測控制器  特徵 點提取  地圖合併  Rao-Blackwellized 粒子濾波器  多車 
英文關鍵字 ROS  autonomous ground vehicle  SLAM  explore  navigation  Model Predict Control  feature extraction  map merging  Rao-Blackwellized Particle filter  Multi-vehicle 
中文摘要 現今,自駕車相關領域的研究愈來愈熱門了,然而即時定位與地圖構建常常是研究自駕車所面臨的第一個挑戰,構建一個值得信任的地圖往往會花費許多的人力和時間,因此找出一個加速構建地圖的方式是十分必要的。而其中一個有效加速構建地圖的方法為利用多車建構不同區域的地圖,而後再合併所有區塊,若車子能自主探索未知領域且合併地圖,這將節省我們很多的時間。這篇論文提出一個完整的自駕車架構,包括有即時定位與地圖構建、探索未知區域、地圖合併、規劃路徑及控制。車子使用Rao-Blackwellized粒子濾波器構建地圖,偵測地圖邊界點並利用Timed-Elastic-Band planner規劃路徑前往之,依循模型預測控制器追蹤軌跡,最後對地圖做特徵點提取,找出不同地圖之間的關係並合併之,把所有的組件整合應用於機器人作業系統,並且在一個規劃好的實驗場地實驗後,我們確實可以在達成多車自主探索並合併地圖。
英文摘要 Nowadays, the research about the area of the autonomous ground vehicle is more popular than before. Simultaneous Localization And Mapping always the first challenge we meet. However, constructing a trusty map cause a lot of time and labor consumptions. Hence it is necessary to figure out a method to speed up the mapping process. In fact, one of the significant methods is that uses multiple vehicles to explore different parts of the map. Once a vehicle can explore and merge maps autonomously, we can save lots of time. This thesis proposes a complete structure for autonomous driving including, SLAM, exploration, map merging, planning, and control. The vehicle can construct maps through the Rao-Blackwellized Particle filter and explores the unknown region according to the frontier points. After that, plan a trajectory from Timed-Elastic-Band planner, and use the Model Predict Control to track the trajectory. Finally, use feature extraction to find out the relationship between each map and merge the maps together. After integrates every module in ROS, we can have the experiment in the laboratory. Eventually, the vehicles can really explore and merge the maps autonomously.
論文目次 Abstract i
中文摘要 ii
致謝 iii
Contents iv
List of figures vi
List of tables viii
List of Abbreviations ix
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Related work 2
1.3 Motivation 3
1.4 Goals 4
Chapter 2 Testbed 5
2.1 Hardware 5
2.2 Software 6
Chapter 3 Rao-Blackwellized Particle Filter 8
3.1 Basic concept 8
3.2 Motion model 9
3.3 Importance weighting 13
3.4 Improved Proposal Distribution 15
3.5 Adaptive Resampling 20
3.6 Global localization 24
3.7 Simulation result 25
Chapter 4 Map Merging 31
4.1 Oriented FAST and Rotated BRIEF 31
4.2 Accerlated KAZE 35
4.3 Feature matching 37
4.4 Communication Network 39
4.5 KITTI data set 41
4.6 Parallel Tracking and Mapping 46
Chapter 5 Explore, Navigation and MPC Control 47
5.1 Frontier exploration 47
5.2 Navigation and planning 49
5.3 Model Predictive Control 50
Chapter 6 Experiment Discussion 52
6.1 Experiment setup 52
6.2 Result and Discussion 55
6.3 Conclusion 60
Bibliography 61
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