标题: | 基于全向式影像之机器人同步定位与环境地图建立 Simultaneous Localization and Mapping Using Omni-Directional Images |
作者: | 黄富圣 Fu-Sheng Huang 宋开泰 Kai-Tai Song 电控工程研究所 |
关键字: | 全向式摄影机;同步定位与地图建立;移动式机器人;扩展卡尔曼滤波;SIFT;Omni-directional camera;SLAM;Mobile robot;EKF;SIFT |
公开日期: | 2007 |
摘要: | 本论文提出一使用全向式影像之机器人定位方法。以全向式摄影机为感测器,结合基于Extended Kalman Filter(EKF)之同时定位与环境地图建立(Simultaneous Localization and Mapping, SLAM)演算法,让机器人在移动的同时,能够建立出环境特征地图并定位出机器人本身的位置。全向式摄影机具有360度的视角,除了能取得更多的环境特征外,亦能增加持续追踪到landmark的时间,让SLAM的运作更为稳定。配合摄影机的特性,本论文参考Scale Invariant Feature Transform(SIFT)演算法发展出一有效之特征点辨识演算法,用以辨识两张相邻的影像中相同的环境特征点,此方法对于影像经过旋转及大小缩放后依然拥有相当稳健的辨识。本论文提出一视觉参考点建立与转换的策略,让机器人进入新的环境时能建立新的参考点与地图,当走回旧地区时则从资料库中取回旧有的参考点资讯使用,减少参考点总数,降低EKF滤波器的运算负担。论文中以实验室之机器人进行导航实验来验证所提出之定位演算法,实验结果显示特征点比对之正确率为90%,行经30公尺后之定位误差为0.1公尺。实验的结果证实机器人能依定位系统的帮助在走廊上长距离的移动,并且同时建立出走廊环境的特征点地图,达成机器人室内导航的功能。 This study investigates robot localization and mapping using omni-directional images. A method is proposed to use an omni-directional camera to realize simultaneous localization and mapping (SLAM) algorithm based on extended Kalman filter (EKF). Because of the 360° field of view, an omni-directional camera is suitable for simultaneous localization and mapping (SLAM) for detecting and tracking environmental features. A new algorithm is developed adopting scale invariant feature transform (SIFT) method to match features in environment between two images. This thesis also presents a switching method of visual reference scans. In this method, reference scan can be added to a database or switched automatically among reference scans. These scans can be used repeatedly to reduce the complexity of extended Kalman filter (EKF). Experiments results show that the matching rate of landmark features is 90%. A long range indoor navigation experiment revealed that the proposed localization algorithm can help robot to navigate in indoor environment and build the features map simultaneously. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009412540 http://hdl.handle.net/11536/80671 |
显示于类别: | Thesis |
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