本研究使用擴張型卡爾曼濾波器(EKF)發展機器人的雙眼視覺式同時自我定位、建圖、與移動物體追蹤(SLAMMOT)系統。本研究主要內容為利用地圖特徵在空間中的位置限制條件，發展不依賴估測器的資料關聯與地圖管理程序，以避免狀態估測錯誤所引起的不良效應。其次利用此位置限制條件，規劃偵測移動物體的演算法。所發展的演算法最後與卡爾曼濾波器整合成為雙眼視覺式EKF SLAMMOT系統，在室內環境中測試，成功執行路徑閉合、SLAM任務、以及移動物體偵測與追蹤的功能。 This thesis presents a visual simultaneous localization, mapping and moving object tracking (SLAMMOT) based on extended Kalman filter (EKF). First, we use the geometric constraints of static landmarks in three-dimensional space to design the algorithms of data association and map management. Since these algorithms are independent of the EKF estimator, the SLAMMOT system can recover from the problem of robot kidnapped automatically. Second, we use the same geometric constraints to develop the algorithm for moving object detection. The developed algorithms are integrated with the EKF estimator to carry out the experiments of SLAMMOT tasks in indoor environments.
