基于旋量理论的变换解耦策略用于带重力先验的确定性点云配准
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
AI总结：
针对带重力先验的点云配准问题，提出基于旋量理论的变换解耦策略，将4自由度问题拆分为三个子问题分别求解，实现了高效确定性配准，在外点率超99%时仍优于现有方法。
摘要：
点云配准在存在大量外点对应关系时极具挑战性。本文聚焦于解决实践中常见的、带有重力先验的基于对应关系的鲁棒配准问题。重力方向通常由惯性测量单元（IMUs）获取，可将旋转的自由度（DOF）从3个减少到1个。我们利用旋量理论提出了一种新颖的变换解耦策略。该策略将原本的4自由度问题分解为三个子问题，分别对应1自由度、2自由度和1自由度，从而提升了计算效率。具体而言，第一个1自由度代表沿旋转轴的平移，我们提出了一种基于区间穿刺的方法来求解它。第二个2自由度代表旋量理论中的辅助变量极点，我们采用分支定界法来求解。最后一个1自由度代表旋转角度，我们提出了一种全局投票法进行估计。所提方法依次求解三个共识最大化子问题，实现了高效且确定性的配准。特别地，由于其极强的鲁棒性，该方法甚至能处理无对应关系的配准问题。在合成数据集和真实世界数据集上的大量实验表明，我们的方法比现有最优方法更高效、更鲁棒，甚至在处理外点率超过99%的情况时依然如此。
英文摘要：
Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
