近幾年來，無人機的應用越來越廣泛，並藉著成熟的飛行控制技術讓無人機逐漸走向應用層面。在這些眾多應用中，定位系統作為一種底層技術，多數應用皆建築其上。因此強健與精準之定位系統是所有無人機應用所追求的性能，以確保在任何情況下達成任務。目前室外無人機過度依賴GPS 定位訊號，而GPS 定位訊號易受干擾，且無法在室內使用，直接影響了無人機的性能表現，也侷限其應用場景。本研究選用深度量測範圍比商用RGB-D 相機更大的雙眼相機系統進行無人機強健定位系統的開發，並基於雙眼平行式追蹤與建圖結構(Stereo Parallel Tracking and Mapping,S-PTAM)進行設計與分析。
由於視覺定位系統在進行觀測時，皆會引入雜訊。為了提高定位強健度，本研究透過慣性測量單位(Inertial Measurement Unit, IMU)量測三軸加速度的資訊，並開發基於移動窗口的靜置偵測演算法，藉此反算精準的初始尤拉角，建立精準視覺定位系統與IMU的初始關係。以Mahony 演算法進行姿態解算，依其結果對視覺定位系統進行位姿預補償(Pre-Compensation)。本文並提出以關鍵幀之間的偏航角(Yaw)與位移差做為決策指標開發匹配閥值的調適律。最後，以EuRoC 資料集進行本文提出之演算法的驗證與成果分析。

The purpose of this paper is to construct a Visual Simultaneous Localization and Mapping (VSLAM) system that based on stereo vision and Inertial Measurement Unit (IMU). So that UAVs can navigate safely in GPS denied environments. And take improving precision, robustness, and versatility as the main research goals.
System based on Stereo Parallel Tracking and Mapping (S-PTAM) is used in this paper. And the overall system consists of two parts, tracking thread and mapping thread which are operated in parallel. Among tracking thread, covisibility and frustum culling are used to select local map. Then through the feature matching and bundle adjustment, estimating camera pose can be achieved. And triangulation algorithm is used to build the map points with stereo image in mapping thread. Finally, the process of optimizing map is executed which can help maintain the accuracy of the map.
In addition to visual information, IMU information is used in this paper in order to provide more accurate initial value for bundle adjustment. In this way, the robustness and positioning frequency of whole system can be improved. According to the difference of displacement and yaw angle between the keyframes, this paper develops online conditional threshold algorithm which will adjustment matching threshold automatically. Finally, whole system is verified by EuRoC dataset.

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