Image Depth Initialization and Fuzzy Data Association for Aerial Robot Monocular Visual Localization and Mapping

• Main Authors: Ting-Wei Chen, 陳庭瑋
• Other Authors: 王銀添
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/07348162565621224367

碩士 === 淡江大學 === 機械與機電工程學系碩士班 === 102 === 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.