针对大范围室外场景和具有重复、高频纹理特征(例如水泥地、草坪)的场景,提出了一种鲁棒性强、定位精度高、速度更快的视觉定位算法。采用8级图像金字塔的ORB(Oriented FAST and Rotated BRIEF)特征描述子提取图像特征点,通过K近邻(KNN...针对大范围室外场景和具有重复、高频纹理特征(例如水泥地、草坪)的场景,提出了一种鲁棒性强、定位精度高、速度更快的视觉定位算法。采用8级图像金字塔的ORB(Oriented FAST and Rotated BRIEF)特征描述子提取图像特征点,通过K近邻(KNN)匹配相邻图像序列的特征点对,依次解算基础矩阵F和本质矩阵E,采用自适应法利用单应矩阵和本质矩阵进行位姿估计,最后解算两帧图像间相机刚体运动的旋转R和平移t,利用三角测量法则求解出匹配点的三维坐标,重建相机运动轨迹。为了提高算法性能,提出采用最小化基于点特征的非线性重投影误差优化三维点。通过调用OpenCV在C++中实现,对所采集的数据集进行测试,测试结果表明,该方法比传统的3D位姿估计更优,实时可行。由于其基于单目而实现,因此无法得到尺度信息。展开更多
This paper presents a new method for simultaneously eliminating visual artifacts caused by moving objects and structure misalignment in image stitching. Given that the input images are roughly aligned, our approach is...This paper presents a new method for simultaneously eliminating visual artifacts caused by moving objects and structure misalignment in image stitching. Given that the input images are roughly aligned, our approach is implemented in two stages. In the first stage, we discover motions between input images, and then extract their corresponding regions through a multi-seed based region growing algorithm. In the second stage, with prior information provided by the extracted regions, we perform a graph cut optimization in gradient-domain to determine which pixels to use from each image to achieve seamless stitching. Our method is simple to implement and effective. The experimental results illustrate that the proposed approach can produce comparable or superior results in comparison with state-of-the-art methods.展开更多
文摘针对大范围室外场景和具有重复、高频纹理特征(例如水泥地、草坪)的场景,提出了一种鲁棒性强、定位精度高、速度更快的视觉定位算法。采用8级图像金字塔的ORB(Oriented FAST and Rotated BRIEF)特征描述子提取图像特征点,通过K近邻(KNN)匹配相邻图像序列的特征点对,依次解算基础矩阵F和本质矩阵E,采用自适应法利用单应矩阵和本质矩阵进行位姿估计,最后解算两帧图像间相机刚体运动的旋转R和平移t,利用三角测量法则求解出匹配点的三维坐标,重建相机运动轨迹。为了提高算法性能,提出采用最小化基于点特征的非线性重投影误差优化三维点。通过调用OpenCV在C++中实现,对所采集的数据集进行测试,测试结果表明,该方法比传统的3D位姿估计更优,实时可行。由于其基于单目而实现,因此无法得到尺度信息。
文摘This paper presents a new method for simultaneously eliminating visual artifacts caused by moving objects and structure misalignment in image stitching. Given that the input images are roughly aligned, our approach is implemented in two stages. In the first stage, we discover motions between input images, and then extract their corresponding regions through a multi-seed based region growing algorithm. In the second stage, with prior information provided by the extracted regions, we perform a graph cut optimization in gradient-domain to determine which pixels to use from each image to achieve seamless stitching. Our method is simple to implement and effective. The experimental results illustrate that the proposed approach can produce comparable or superior results in comparison with state-of-the-art methods.
