本論文研究以影像為基礎的同步定位與地圖建立演算法(Simultaneous Localization and Mapping, SLAM)，並將之使用於無人載具上。無人載具是最近很熱門的一個研究領域，它可以被應用於很多方面，例如工廠，軍事和不安全的環境等，使用無人載具時，最重要的為如何導引他往所需之目標安全的前進，為了達成這個目標，所以它必須要知道自己身在何處，周遭環境為何，然而同步定位與地圖建立演算法包含這兩部分。
我們採用立體攝影機來實現SLAM演算法，我們利用立體視覺攝影機快速計算距離的特性，來測量無人載具到地標(Landmark)之距離，並使用快速的特徵點偵測及匹配的演算法-快速強健特徵點。當無人載具不斷移動時，可由攝影機獲得影像以獲取無人載具及地標的位置等預測數據，搭配擴展式卡爾曼濾波器(Extended Kalman Filter)來完成載具與地標間相對位置的估測，而當地圖中的特徵點被反覆量測時，地標分佈的不確定性(Gaussian uncertainty)會持續收斂至確定值，建立靜止地標分佈地圖，此地圖可用以提供無人載具的定位及周遭環境的資訊。

This thesis studies the Simultaneous Localization and Mapping (SLAM) algorithm bases on image and uses it on mobile robotics. Mobile robots are computer-controlled devices that have the capability to move around in an unknown environment. Mobile robots are extensively applied in fields such as industry, military and unsafe environments. One of the important topics for research and development of mobile robots researchers is how to guide the vehicle automatically and safely move from one place to another, so it must know where it is and what its surround is.
In this algorithm, we use stereo camera to acquire two images of environment in the same time, and use Speeded-Up Robust Features (SURF) algorithm to detect and match features on the image. By way of similar relationship, the distance between the landmark and the robot can be calculated by the pair of images. Estimations of vehicle motion and landmarks’ positions are computed within a standard Extended Kalman Filter frame work. The robot will continuously move, execute the localization step, and then update the map. This map can provide mobile robotics localization and navigation.

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