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.

中文翻译：

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CLEAR：用于多视图数据关联的一致提升、嵌入和对齐校正算法

许多机器人应用需要对齐和融合在多个视图中获得的观察结果，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它们的一致性。然而，解决这个计算上具有挑战性的问题的现有方法对于实时应用来说通常太慢了。此外，现有的一些技术可能会违反循环一致性原则，从而大大降低融合精度。这项工作提出了 CLEAR（一致提升、嵌入和对齐校正）算法来解决这些问题。通过利用多路匹配和谱图聚类文献中的见解，CLEAR 以计算效率高的方式提供循环一致和准确的解决方案。对合成数据集和真实数据集进行了数值实验，以证明我们的算法在实际问题中的可扩展性和卓越性能。该算法框架可以显着提高现有离散分配问题的准确性和效率，这些问题传统上使用成对（但可能不一致）的对应关系。CLEAR 的实施已在网上公开发布。