无需配对点，直接搜索最优旋转与平移，实现鲁棒的点云配准。
A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration
- 通过半穷举法遍历旋转矩阵，高效求解对应平移
- 在模拟ModelNet40数据集上优于当前最先进方法
- 适合需要高精度姿态估计的机器人实操场景
点云配准旨在寻找两个点云之间的刚性变换以实现对齐，是机器人学与计算机视觉中的关键问题。本文提出一种直接半穷举搜索（DSES）算法，不依赖对应关系，通过算法简洁但计算密集的半穷举搜索策略，在现代GPU上实现高度并行化。DSES遍历潜在的旋转矩阵，并为每个旋转快速计算最大化内点数的对应平移；随后基于任意距离度量，直接计算每组变换{R, t}的误差，从而确定最优刚性变换。利用现代GPU的并行能力，DSES在模拟的ModelNet40基准上显著优于当前最先进方法，并在真实机器人姿态估计任务中展现出优异性能与鲁棒性（视频演示：https://youtu.be/q0q2-s2KSuA）。
原文摘要 · Abstract (English)
Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate $\{R, t\}$. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).
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