隨著科技進步，無人機開始應用於很多領域，但是有些區域不能依賴傳統的GPS導航，例如室內場景、橋樑檢測。視覺慣性里程計(VIO)是一個熱門的無GPS導航的研究主題，然而視覺慣性里程計在相關研究中有著尺度與長途誤差的問題。本研究提出一個方式，在初始的過程中，只偵測已知地標的四個已知尺度的角點，利用這四個已知距離的角點得出相對準確的位置，並使用視覺慣性里程計估測出的位置與已知地標估測出的相對準確的位置用最小平方的方式進行尺度修正，以及使用擴展卡爾曼濾波器做整合，預測階段使用慣性測量單元做積分，量測階段分兩部分，在估測尺度過程中與回程看到已知地標使用已知地標估測出的視覺里程計做修正以消除長途誤差，在完成估測尺度後，使用尺度修正後的視覺慣性里程計做修正。實驗初期在地面測試做初步驗證，本研究使用自組的無人機裝上視覺慣性感測器收集資料，並整合高精度RTK做軌跡驗證。最後應用於飛行測試，實驗結果顯示本研究提出的方式有效地解決視覺慣性里程計的尺度與長途誤差問題。

With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and can be applied in many areas. However, there are some environments where GPS is not available or has the problem of GPS signal outage, such as indoor inspections and bridge inspections. Visual Inertial Odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has the problems of scale errors and long-term drift discussed in the other studies. This study proposes a method to correct the position errors of VIO without the help of GPS information. In the initial process, only corners of known landmarks are detected and utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO, and then the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale by using the least square method. The Inertial Measurement Unit (IMU) is used for integration in the prediction phase. The measurement update is divided into two parts. When the scale is being estimated or UAV is close and back to the takeoff location, the visual odometer outputs are integrated with estimated positions from the known landmarks to make corrections to eliminate long-term drift. After the scale estimation is completed, the VIO with scale correction is used. At the beginning of the experiment, preliminary verification was conducted on the ground. In this study, a self-assembled UAV equipped with a visual inertial sensor was used to collect data and integrated high-precision RTK for trajectory verification. Finally, it is applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problem of scale and long-term drift of VIO.

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