Point cloud registration plays a crucial role in processing large-scale building point cloud data. However, existing registration algorithms face challenges in effectively handling outliers in descriptor-based correspondence. This paper presents an automatic registration method for large-scale building point clouds that is capable of achieving swift and accurate registration without the need for initial guessing. The method employs a two-step matching optimization approach: coarse (two-point)-to-fine (three-point), selecting matches based on two-point reliability and three-point consistency. Spatial transformation parameters are broken down into rotations and translations. A progressively optimized kernel function is proposed for estimating rotation, while a clustering confidence algorithm computes translation. Comprehensive experiments were conducted using real-world data. The results indicate that the approach swiftly and accurately estimates optimal outcomes when processing large-scale building point clouds with outlier rates up to 99%. Compared to six existing registration methods, the proposed approach reduces rotation error by 6.15% and translation error by 12.83%, while improving efficiency by 2.57%.

中文翻译：

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自动配准具有高异常值率的大型建筑物点云


点云配准在处理大规模建筑点云数据方面起着至关重要的作用。然而，现有的配准算法在有效处理基于描述符的对应中的异常值方面面临挑战。本文提出了一种用于大规模建筑点云的自动配准方法，该方法能够实现快速准确的配准，而无需初始猜测。该方法采用两步匹配优化方法：粗 （2 点） 到精细 （3 点），根据 2 点可靠性和 3 点一致性选择匹配项。空间变换参数分为旋转和平移。提出了一种渐进优化的核函数来估计旋转，而聚类置信度算法计算平移。使用真实世界数据进行了综合实验。结果表明，当处理异常值率高达 99% 的大规模建筑点云时，该方法能够快速准确地估计最佳结果。与现有的 6 种配准方法相比，所提方法将旋转误差降低了 6.15%，平移误差降低了 12.83%，同时效率提高了 2.57%。