Point Cloud Registration is a fundamental problem in robotics and computer vision. Due to the limited accuracy in the matching process of 3D keypoints, the presence of outliers, probably in very large numbers, is common in many real-world applications. In this paper, we present ICOS (Inlier searching using COmpatible Structures), a novel, efficient and highly robust solution for the correspondence-based point cloud registration problem. Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures. In this manner, even with extreme outlier ratios, inliers can be detected and collected for solving the optimal transformation, leading to our robust registration solver ICOS. Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99% outliers with nearly 100% recall ratio of inliers whether the scale is known or unknown, outperforming other state-of-the-art methods, (ii) ICOS is practical for use in real-world applications.

中文翻译：

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ICOS：具有对应关系的高效且高度健壮的点云注册

点云注册是机器人技术和计算机视觉中的一个基本问题。由于3D关键点匹配过程中的精度有限，因此在许多实际应用中很常见异常值（可能数量非常大）。在本文中，我们提出了ICOS（使用Compatible结构进行内部搜索），这是一种新颖，高效且高度健壮的解决方案，用于解决基于对应关系的点云注册问题。具体来说，我们（i）针对注册问题提出并构建了一系列兼容的结构，其中可以建立各种不变量，并且（ii）设计两个省时的框架，一个用于已知规模的注册，另一个用于未知规模的注册。 ，以滤除离群值并从基于兼容结构的不变约束随机采样中寻找离群值。以这种方式，即使存在极高的异常值比率，也可以检测和收集异常值，以解决最佳转换问题，从而实现我们强大的配准解算器ICOS。通过在标准真实数据集上进行的大量实验，我们证明：（i）我们的求解器ICOS快速，准确，鲁棒地针对多达99％的异常值，无论规模是已知还是未知的，异常值的召回率均接近100％，优于其他状态（ii）ICOS在实际应用中是实用的。