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。

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.

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