标题: 基于扩展卡门滤波同时定位与地图建立之地图接合研究
Study of Map Joining in EKF-SLAM
作者: 刘建宏
宋开泰
电控工程研究所
关键字: 同时定位与地图建立;卡曼滤波器;地图接合;封闭路径;EKF-SLAM;Map join;Loop closure;Kinect
公开日期: 2011
摘要: 本论文提出一使用Kinect深度摄影机之机器人定位方法。以Kinect为感测器取得环境资讯,结合Extended Kalman Filter(EKF) 之同时定位与环境地图建立(Simultaneous Localization and Mapping, SLAM)演算法,并以地图接合之方式降低定位系统运算复杂度。Kinect深度影像摄影机同时提供彩色影像与距离资讯,本研究以撷取SURF特征点对应感测器所取得之深度关系,快速且精确的取得特征点之环境资讯,接着以EKF修正机器人状态与特征点三维座标。为了避免EKF随环境增长而使矩阵运算复杂度快速增加,本论文提出以区域路径范围判断之作法将环境分为数个子区域,机器人仅需要使用区域内之特征点讯息,而无须使用整个环境资讯做定位系统演算,如此提高定位系统于机器人应用之即时运算之性能,最后邻近的子区域以地图融合演算法修正其间之差异,以维持整个环境地图之完整性。实验结果显示机器人运行于一16mX7m之室内环境行走约83公尺,当机器人回到原点附近时实际位置与估测之间的二维座标平均误差小于0.1公尺。实验结果证实机器人能以EKF之定位系统藉地图接合之作法,达成机器人室内导航之功能。
This study investigates simultaneous localization and mapping(SLAM) of a mobile robot using a Kinect depth camera. Depth and image information from Kinect are utilized to realize SLAM algorithms based on extended Kalman filter(EKF). In this thesis, visual landmarks are extracted by SURF algorithm, then three dimensional location of feature points are calculated from Kinect depth image data. A map joining method is proposed to reduce computational complexity of EKF-SLAM, and to correct the deviations of adjacent local maps. A global map of the environment is constructed by the map joining procedure. Navigation experiments show that the accuracy of robot localization for a travel about 83m path is within 0.1m. It is verified that the developed algorithm of simultaneous localization and mapping with map joining can allow robot to navigate in an indoor environment
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079812594
http://hdl.handle.net/11536/46950
显示于类别:Thesis


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