教師資料查詢 | 類別: 會議論文 | 教師: 王銀添 Wang Yin-tien (瀏覽個人網頁)

標題:Fuzzy Data Association of Aerial Robot Monocular SLAM
學年105
學期1
發表日期2016/08/16
作品名稱Fuzzy Data Association of Aerial Robot Monocular SLAM
作品名稱(其他語言)
著者Wang, Yin-Tien; Chen, Ting-Wei
作品所屬單位
出版者
會議名稱The 3rd International Conference on Machine Vision and Machine Learning (MVML'16)
會議地點Budapest, Hungary
摘要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, a 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. The contribution of this study are twofold. First, an efficient data association method is developed to determine the robust landmarks for robot mapping. Second, an ultrasonic sensor is used to provide one-dimensional distance measurement and solve the map scale determination problem of monocular vision. Meanwhile, 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 software program of the robot navigation system is developed in a PC-based controller. 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.
關鍵字Visual localization and mapping;Aerial robot navigation;Detection of image features;Robot vision
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20160816~20160817
通訊作者
國別匈牙利
公開徵稿
出版型式
出處Proceedings of the 3rd International Conference on Machine Vision and Machine Learning (MVML'16), pp.MVML 103
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