基于LRF传感器的SURF算法移动机器人室内定位

Zhitao Wang, Yo-Seop Hwang, Yun-ki Kim, Donghyuk Lee, Jangmyung Lee
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引用次数: 3

摘要

本文提出了一种基于室内环境下深度图像的移动机器人位置估计方法——SURF (accelerated Robust Features)算法。从SIFT算法衍生而来的SURF算法具有计算速度快、鲁棒性强等优点。深度图像由一个2D激光测距仪传感器生成,该传感器被控制绕其y轴旋转。根据每一帧深度图像中的兴趣点,找出每两帧之间的位置关系,并在对应的兴趣点之间进行匹配。在实验中,我们将展示使用本文提出的方法实现位置估计。实验结果也证明了该方法的准确性和有效性。
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Mobile robot indoor localization using SURF algorithm based on LRF sensor
In this paper we proposed a method to implement estimation of mobile robot's position by using SURF (Speeded Up Robust Features) algorithm based on depth image in indoor environment. SURF which is derived from SIFT algorithm has the advantage of fast calculation speed and a strong robustness. The depth image is generated from a 2D LRF (Laser Range Finder) sensor which is controlled to rotate around its y-axis. According to the interest points in each frame of depth image, we find the position relationship between each two frames and make a match between the corresponding interest points. In the experiment, we will show the implementation of position estimation using the method we proposed in this paper. The accuracy and efficiency of the proposed method has also been proved in the experiment.
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