由於智慧型機器人領域的發展，機器人被賦予了許多能力，從最基本的取放動作，以至於移動能力，並進而往自主移動發展。在機器人與週遭環境物體之間建立其空間上的關係為環境感知的重要基礎，同步定位與地圖建置(simultaneous localization and mapping, SLAM)即為其環境感知的重大突破。
以視覺為基礎的同步定位與地圖建置(vision based-SLAM)，為近幾年來研究的熱門議題。vision-based SLAM主要由視覺感測器獲取場景影像，並在其中擷取具代表性的邊緣、轉角…等基礎特徵作為定位與地圖建置的依據，由於影像感測器對特徵的觀察與角度並非固定，特徵將有縮放、旋轉及形變的差異而造成匹配上的困難與地圖建置上的誤差。本研究的目的便在探討如何利用攝影機進行同步定位與地圖建置，並針對文線上特徵擷取與匹配的問題，提出以SURF (speeded-up robust features)的特徵擷取方法，以提升機器人同步定位與地圖建置的強健性。

Due to the development of the area of intelligent robots which are given many abilities, such as basic actions of picking up and putting down, motion, and moving by itself. It is an important basis for building the spatial relationship between robots and surrounding environment, so simultaneous localization and mapping (SLAM) is a key point of scene perception.
Vision based SLAM technique is recently a popular issue. Vision based SLAM technique is majorly in capturing scene images through vision sensors, and extract basic representative features, such as edges and corners to localization and mapping. However, image sensors are not fixed for feature observation and angles, and these features may cause difficulties of matching and errors of SLAM due to differences of scale, rotation, and shape diversity. This study discusses how to use cameras to implement SLAM, and we propose SURF to increase robustness of SLAM for robots according to feature extraction and matching problems in references.

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