基于单目视觉的移动机器人全局定位

Monocular-Vision-Based Mobile Robot Global Localization

  • 摘要: 提出在基于单目视觉创建的环境地图中实现移动机器人全局定位.基于KD树的最近邻搜索实现特征匹配.应用尺度不变特征变换(SIFT)方法提取特征,并用多维向量描述,保证了对图像光强变化、尺度缩放、三维视角和噪声具有不变性.提出了一种基于RANSAC的鲁棒定位方法.在实际室内环境Pioneer3机器人上进行的实验表明本文提出方法高效、可靠.

     

    Abstract: An environmental map built with monocular vision is used to implement mobile robot global localization.The feature matching is implemented with the KD-treebased nearest search approach.The features are extracted with Scale Invariant Feature Transform(SIFT),and discribed with highly distinctive multi-dimensional vector,making features be invariant to changes in illumination,scale,3D viewpoint and noise.A robust localization based on RANSAC(RANdom SAmple Consensus) approach is presented.Experiments on robot Pioneer 3 with monocular CCD camera in our real indoor environment show that our method is of high precision and stability.

     

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