本論文探討慣性量測元件輔助單眼視覺同時定位與建圖的議題。本論文使用加速強健特徵(Speed-Up Robust Feature, SURF)演算法偵測並描述特徵點，再以反深度參數化方法描述地標點位置，並利用擴張型卡爾曼濾波器(extended Kalman filter, EKF)估測攝影機及地標點狀態。使用慣性量測元件的位移可以推測出尺度基準，並能以此初始化單眼視覺估測，並透過實驗證實慣性量測元件能使單眼視覺同時定位與建圖成功初始化。 This study investigates the issues of inertial measurement unit (IMU) assisted monocular simultaneous localization and mapping (SLAM). The speeded-up robust features (SURF) algorithm is used for interest point detection and description. The positions of environment landmarks are represented by inverse depth parameterization method. The positions of camera and landmarks can be estimated by using the extended Kalman filter (EKF). The map scale for monocular SLAM initialization can be estimated by the displacement of IMU. The experiment results demonstrate that the IMU successfully initialize monocular SLAM.
