SANDRO通过分裂策略提升点云配准鲁棒性，应对高异常值场景。
SANDRO: a Robust Solver with a Splitting Strategy for Point Cloud Registration
- 采用IRLS框架与分阶段非凸优化结合，增强对异常值的容忍度。
- 在真实数据集上成功率达现有方法20%以上，在合成数据上提升60%。
- 适合导航、机器人等需高鲁棒性配准的实时应用。
点云配准是计算机视觉与机器人领域（尤其导航）的关键问题。现有方法在高异常值率或收敛慢时表现不佳。本文提出SANDRO（基于非凸与鲁棒优化的点云分裂配准算法），结合迭代重加权最小二乘（IRLS）与具有渐进非凸性的鲁棒损失函数，并引入分裂策略以应对高异常值率和异常值分布偏斜的问题。该方法有效克服了高异常值率与点云对称性导致的收敛困难。在红木（Redwood）真实数据集上，相比当前最优方法，成功率提升20%；在合成数据上提升达60%。
原文摘要 · Abstract (English)
Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point cloud registration called SANDRO (Splitting strategy for point cloud Alignment using Non-convex anD Robust Optimization), which combines an Iteratively Reweighted Least Squares (IRLS) framework with a robust loss function with graduated non-convexity. This approach is further enhanced by a splitting strategy designed to handle high outlier rates and skewed distributions of outliers. SANDRO is capable of addressing important limitations of existing methods, as in challenging scenarios where the presence of high outlier rates and point cloud symmetries significantly hinder convergence. SANDRO achieves superior performance in terms of success rate when compared to the state-of-the-art methods, demonstrating a 20% improvement from the current state of the art when tested on the Redwood real dataset and 60% improvement when tested on synthetic data.
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