Binocular Vision-SLAM Using Improved SIFT Algorithm
Binocular Vision-SLAM Using Improved SIFT Algorithm
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使用改进的SIFT算法的双目视觉-SLAM
DOI：
10.1109/iwisa.2010.5473273
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Daixian Zhu
中科院分区：
文献类型：
--
作者：
Daixian Zhu
将尺度不变特征变换（SIFT）算法应用于基于视觉信息的移动的机器人同时定位与地图构建（SLAM）中。但该算法复杂，计算时间长。两个改进，以优化其性能。首先将城市街区距离和棋盘距离的线性组合作为相似性度量，然后利用部分特征进行匹配，利用扩展卡尔曼滤波融合SIFT特征信息和机器人信息完成SLAM。仿真实验表明，该方法计算复杂度低，在室内环境下具有较高的定位精度。
SIFT (Scale Invariant Feature Transform) algorithm is used in mobile robot Simultaneous Localization and Mapping (SLAM) based on visual information. But this algorithm is complicated and computation time is long. Two improvements are introduced to optimize its performance. Firstly, the linear combination of cityblock distance and chessboard distance is comparability measurement; secondly, partial features are used to matching .SLAM is completed by fusing the information of SIFT features and robot information with EKF. The simulation experiment indicate that the proposed method reduce computational complexity, and with high localization precision in indoor environments.