本論文探討視覺式感測器輔助飛行機器人巡航的議題，主要的任務是輔助飛行機器人進行定位與建立環境地圖，並且應用在全球定位系統無法順利運作的環境之中。當飛行機器人在環境中巡航時，視覺式感測器可以提供機器人狀態估測與建立環境地圖所需的量測訊息。考量飛行機器人的承載能力，本論文使用單顆攝影機，並且將影像以無線方式傳送到PC-based控制器進行處理。狀態估測器方面，使用擴張型卡爾曼過濾器，遞迴地預測與估測飛行機器人與環境靜態物件的狀態。單視覺影像特徵方面，使用反深度參數化方法描述影像深度，實現非延遲影像特徵初始化的程序。本論文有兩個主要研究成果，第一，針對單視覺影像深度估測的問題，使用超音波感測器提供一維的量測訊息，做為特徵座標初始化的依據。第二，規劃模糊資料關聯的程序，改善特徵地圖管理的效能。本研究在PC-based控制器內建立所需的發展環境，以Visual C++程式語言整合視覺感測器、影像處理、與狀態估測器。整合的系統應用於執行飛行機器人的同時定位與建圖之任務。 This study investigates the issues of visual sensor assisted aerial robot navigation. The major objectives are to provide the aerial robot the capabilities of localization and mapping in global positioning system (GPS) denied environments. When the aerial robot navigates in a GPS-denied environment, the visual sensor could provide the measurement for robot state estimation and environmental mapping. Considering the carrying capacity of the aerial robot, single camera is used in this study and the image is transmitted to PC-based controller for image processing using a radio frequency module. The extended Kalman filter is used as the state estimator to recursively predict and update the states of the aerial robot and the environment landmarks. For the monocular vision sensor, the image depth is represented by using the inverse depth parameterization method and the image features initialization is achieved by a non-delayed procedure. The results of this study are twofold. First, an ultrasonic sensor is used to provide one-dimensional distance measurement and solve the image depth estimation problem of monocular vision. Second, a novel data association procedure is designed based on fuzzy system in order to improve the performance of map management. The software program of the robot navigation system is developed in a PC-based controller using Microsoft Visual Studio C++. The navigation system integrates the sensor inputs, image processing, and state estimation. The resultant system is used to perform the tasks of simultaneous localization and mapping for aerial robots.
