現今全球定為系統 (Global Position System, GPS)已發展多年，並且廣泛運用室外定位及導航，但由於衛星訊號因遭受高樓大廈及建築物遮蔽等因素而無法正常使用於室內空間，進而發展出許多室內定位的方法。室內環境複雜，除常見的聲納及雷射測距方法外，本論文主要研究為使用影像同步定位與建構室內空間演算法 (Simultaneous Localization and Mapping, SLAM)為主，並結合慣性量測元件 (Inertial measurement unit, IMU)為輔建立室內定位系統。隨著載具不斷移動時，單視覺影像同步定位與建構系統，量測特徵點與鏡頭的景深距離，搭配擴展式卡爾曼濾波器(Extended Kalman Filter)提供即時Local frame的位置、速度及姿態等資訊，慣性量測元件則提供相機即時的加速度及姿態資訊作為改善單視覺影像取得過少特徵點而造成的誤差。最後本論文利用多感測器結合演算法，多頻率卡爾曼濾波器(Multi-rate Kalman Filter)結合兩者感測器所提供之資訊整合輸出。實驗部分則使用BCB來完成特徵點的偵測和比對方法，使用函式庫執行Virtual Studio C# 收取MonoSLAM及IMU即時量測資訊，並利用Matlab做資料分析及驗證。

GPS (Global Positioning System) dependent positioning and navigation has been developed over recent years, and has been widely used for outdoor positioning and navigation. However, high-rise buildings or indoor environments can block the satellite signal. Therefore, many indoor positioning methods have been developed to respond to this issue. In addition to measuring the distance using sonar and laser, this research uses monocular simultaneous localization and mapping (MonoSLAM) combined with an inertial measurement unit (IMU) to build an indoor positioning system. As time continues to move a vehicle, MonoSLAM measures the distance between the image features and the camera (depth). Making use of the Extend Kalman Filter (EKF), MonoSLAM provides real-time position, velocity and camera attitude. Because the feature points will not always appear and can't be trusted at all times, a wrong estimation will cause the position to diverge. The integrated system in this thesis uses the multi-rate Kalman Filter to complement each method. Finally, the experiment using Virtual Studio C# is shown to measure the MonoSLAM data and IMU data, and Matlab is used to verify the results.

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