本論文規劃單眼視覺式同時定位與建圖(simultaneous localization and mapping, SLAM)系統的地圖建立程序，處理包括影像特徵偵測方法以及量測與地圖的資料關聯等問題。影像特徵偵測方面，將偵測快速強健特徵(Speeded-Up Robust Features, SURF)，以高維度特徵描述向量描述地圖特徵，並且建立特徵式地圖。資料關聯方面，將以地圖特徵在空間位置的預測規劃追蹤視窗，再使用最鄰近(nearest-neighbor, NN)方法進行量測資料與地圖特徵的高維度特徵描述向量之搜尋比對，也將使用Lucas-Kanade演算法輔助在影像平面上的特徵之追蹤。最後，也將擴充所發展的單眼視覺式SLAM系統之功能，使之具備偵測與追蹤移動物體(detection and tracking of moving object, DATMO)的功能，可以執行同時定位、建圖、與移動物體追蹤(simultaneous localization mapping, and moving object tracking, SLAMMOT)的任務。 In this thesis, the algorithms of map building are developed for a simultaneous localization and mapping (SLAM) system with monocular vision. The algorithms include the method of feature detection and data association in between the measured features and features in the map. For the detection of image features, the speeded-up robust features (SURF) with high-dimensional description vectors are utilized to describe the map features, and build the feature-based map. In data association, a tracking window is planned based on the prediction of map features in spatial location, and then the nearest neighbor method is employed to match the high-dimensional descriptor vector of the measured features with that of the features in the map. The Lucas-Kanade algorithm is also utilized to improve the performance of feature tracking in the image plane. Finally, the developed SLAM system with monocular vision is integrated with the function module of detection and tracking of moving objects (DATMO), in order to perform the tasks of simultaneous localization mapping, and moving object tracking (SLAMMOT).
