首先於想要的範圍場景中置放兩個(以上)地標，並且此場景內無障礙物阻擋自走車的運動且無劇烈的光線變化以影響相關影像處理，經由特徵點之萃取、描述、比對三步驟進行地標的辨識。為達到即時的目的，以FPGA硬體進行相關影像演算法(即FDM(Feature Detection and Matching)之計算、馬達PWM驅動訊號及馬達位置解碼。為了達到節省特徵點數量及資源消耗，以Harris Corner Feature Detection (HCFD)法則取代以SIFT(Scale Invariant Feature Transform)方法的特徵點之萃取。緊接著，建立地標之特徵點的描述器(Feature Descriptor)，將其特徵向量，儲存到FPGA平台中，以利於真實地標的比對。搜索及辨識所在場景中的可能地標，並估測其於世界座標系統之座標，完成所有於場景中的地標之位置圖(Map Construction)。並以軟體執行較難以達到即時效能或為保持彈性的計算 (即自走車搜索策略、刪除誤判特徵點、馬達PID控制器)。最後，以所建立的地標位置圖進行自走車視覺導引的任務，經由相關的實驗以驗證所建議的方法之可行性及有效性。

In this thesis, an FPGA-based feature detection and matching (FDM) algorithm is first implemented to accelerate the recognition and description of the features of landmarks. For the localization of mobile robot (MR), some selected landmarks (e.g., extinguisher, garbage can with suitable Chinese characters) are first trained by the proposed FDM algorithm to accomplish their corresponding feature vectors stored in the FPGA platform. These landmarks are unknown distribution in an environment for the localization of MR. After the matching with the pre-trained feature vector of landmarks, the 2D coordinate of the corresponding landmark is estimated and then stored into a map, so that it can be as a reference map with known landmarks for the visual navigation of an MR. Since the location of landmarks is unknown, an on-line searching strategy of MR is designed. For fully development of the proposed platform, the PID control of two motors using kinematics of MR, the removal of unsuitable feature points to improve the accurate localization of landmark, and the searching strategy of specific landmarks are arranged in the NIOS with the software manner. On the other hand, image capturing, processing, extraction of feature points, feature descriptor, and landmark matching are executed by FPGA hardware. Finally, the corresponding experiments are given to validate the effectiveness and practicality of the proposed methodology.

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