Binocular stereovision has become one of the development trends of machine vision and has been widely used in robot recognition and positioning. However, the current research on omnidirectional motion handling robots at home and abroad is too limited, and many problems cannot be solved well, such as single operating systems, complex algorithms, and low recognition rates. To make a high-efficiency handling robot with high recognition rate, this article studies the problem of robot image feature extraction and matching and proposes an improved speeded up robust features (SURF) algorithm that combines the advantages of both SURF and Binary Robust Independent Elementary Features. The algorithm greatly simplifies the complexity of the algorithm. Experiments show that the improved algorithm greatly improves the speed of matching and ensures the real-time and robustness of the algorithm. In this article, the problem of positioning the target workpiece of the robot is studied. The three-dimensional (3-D) reconstruction of the target workpiece position is performed to obtain the 3-D coordinates of the target workpiece position, thereby completing the positioning work. This article designs a software framework for real-time 3-D object reconstruction. A Bayesian-based matching algorithm combined with Delaunay triangulation is used to obtain the relationship between supported and nonsupported points, and 3-D reconstruction of target objects from sparse to dense matches is achieved.

中文翻译：

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双目立体视觉全向运动搬运机器人

双目立体视觉已成为机器视觉的发展趋势之一，并已广泛应用于机器人识别与定位。然而，目前国内外对全向运动搬运机器人的研究过于有限，存在操作系统单一、算法复杂、识别率低等诸多问题无法很好解决。针对机器人图像特征提取与匹配问题，为制作具有高识别率的高效搬运机器人，提出了一种改进的加速鲁棒特征（SURF）算法，该算法结合了SURF和Binary Robust Independent Elementary Features的优点。 . 该算法大大简化了算法的复杂度。实验表明，改进后的算法大大提高了匹配速度，保证了算法的实时性和鲁棒性。本文研究了机器人目标工件的定位问题。对目标工件位置进行三维（3-D）重建，得到目标工件位置的3-D坐标，从而完成定位工作。本文设计了一个用于实时 3-D 对象重建的软件框架。采用基于贝叶斯的匹配算法结合Delaunay三角剖分获得支持点与非支持点的关系，实现目标对象从稀疏匹配到密集匹配的3D重建。研究了机器人目标工件的定位问题。对目标工件位置进行三维（3-D）重建，得到目标工件位置的3-D坐标，从而完成定位工作。本文设计了一个用于实时 3-D 对象重建的软件框架。采用基于贝叶斯的匹配算法结合Delaunay三角剖分获得支持点与非支持点的关系，实现目标对象从稀疏匹配到密集匹配的3D重建。研究了机器人目标工件的定位问题。对目标工件位置进行三维（3-D）重建，得到目标工件位置的3-D坐标，从而完成定位工作。本文设计了一个用于实时 3-D 对象重建的软件框架。采用基于贝叶斯的匹配算法结合Delaunay三角剖分获得支持点与非支持点的关系，实现目标对象从稀疏匹配到密集匹配的3D重建。本文设计了一个用于实时 3-D 对象重建的软件框架。采用基于贝叶斯的匹配算法结合Delaunay三角剖分获得支持点与非支持点的关系，实现目标对象从稀疏匹配到密集匹配的3D重建。本文设计了一个用于实时 3-D 对象重建的软件框架。采用基于贝叶斯的匹配算法结合Delaunay三角剖分获得支持点与非支持点的关系，实现目标对象从稀疏匹配到密集匹配的3D重建。