Distributed and consistent multi-image feature matching via QuickMatch
Distributed and consistent multi-image feature matching via QuickMatch
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DOI：
10.1177/0278364920917465
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发表时间：
2019-10
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通讯作者：
Zachary T. Serlin;Guang Yang;Brandon Sookraj;C. Belta;Roberto Tron
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作者：
Zachary T. Serlin;Guang Yang;Brandon Sookraj;C. Belta;Roberto Tron
在这项工作中，我们考虑了多图像的机器人分布式网络中的目标匹配问题。多图像特征匹配是同步定位与地图构建、单应性、目标检测、运动恢复结构等应用的关键。我们首先回顾了快速匹配算法的多图像特征匹配。然后，我们提出了NetMatch，一种算法，用于在计算单元（代理）中分配功能集，该算法在很大程度上保留了功能匹配质量，并最大限度地减少了代理之间的通信（特别是避免将所有数据泛洪到所有代理）。最后，我们提出了一个实验应用程序的QuickMatch和NetMatch的对象匹配测试与低质量的图像。QuickMatch和NetMatch算法与其他标准匹配算法的匹配一致性的保存进行了比较。我们的实验表明，QuickMatch和Netmatch可以扩展到更大数量的图像和特征，并且比标准技术更准确地匹配。
In this work, we consider the multi-image object matching problem in distributed networks of robots. Multi-image feature matching is a keystone of many applications, including Simultaneous Localization and Mapping, homography, object detection, and Structure from Motion. We first review the QuickMatch algorithm for multi-image feature matching. We then present NetMatch, an algorithm for distributing sets of features across computational units (agents) that largely preserves feature match quality and minimizes communication between agents (avoiding, in particular, the need to flood all data to all agents). Finally, we present an experimental application of both QuickMatch and NetMatch on an object matching test with low-quality images. The QuickMatch and NetMatch algorithms are compared with other standard matching algorithms in terms of preservation of match consistency. Our experiments show that QuickMatch and Netmatch can scale to larger numbers of images and features, and match more accurately than standard techniques.