CLEAR：一种用于多视图数据关联的一致性提升、嵌入和对齐校正算法
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
摘要：
许多机器人应用需要在多个视图处获得的观测的对准和融合，以形成环境的全局模型。多路数据关联方法提供了一种机制来提高成对关联的对齐精度并确保其一致性。然而，现有的方法，解决这个计算上具有挑战性的问题往往是太慢的实时应用程序。此外，一些现有的技术可以违反周期一致性原则，从而大大降低了融合精度。这项工作提出了CLEAR（一致提升，嵌入和对齐纠正）算法来解决这些问题。通过利用来自多路匹配和谱图聚类文献的见解，CLEAR以计算高效的方式提供周期一致和准确的解决方案。在合成数据集和真实数据集上进行了数值实验，以证明我们的算法在现实世界中的问题的可扩展性和优越的性能。这种算法框架可以提供显着改善现有的离散分配问题，传统上使用成对（但可能不一致）的对应关系的准确性和效率。CLEAR的执行情况已在网上公布。
英文摘要：
Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues. By leveraging insights from the multi-way matching and spectral graph clustering literature, CLEAR provides cycle consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
