通过动态更新局部对应关系，提升点云配准精度与速度
Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets
- 用角度直方图和线向量长度保持构建初始局部对应集
- 每轮迭代后概率性更新局部对应集，误差降低超10%
- 双RANSAC结构配合全局早停机制，兼顾效率与精度
点云配准（PCR）是遥感应用中整合三维观测数据的基础任务。本文提出一种快速高效的PCR算法，结合概率自更新局部对应关系与线向量集。其双层RANSAC交互模型包含全局RANSAC评估全局对应集，以及在动态更新的局部集上运行的局部RANSAC。初始局部集通过角度直方图统计与线向量长度保持技术构建。为提升精度，引入概率自更新策略，在每轮交互后优化局部集。为降低运行时间，设计全局早停条件，在精度与效率间实现最优平衡。最终通过加权奇异值分解求解配准结果。在公开数据集上的评估表明，该算法在时间效率上表现优异，且根均方误差至少优于当前最优方法10%。C++源码已公开于 https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration。
原文摘要 · Abstract (English)
Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a local RANSAC operating on dynamically updated local sets. Initially, these local sets are constructed using angle histogram statistics and line vector length preservation techniques. To improve accuracy, a probabilistic self-updating strategy refines the local sets after each interaction round. To reduce runtime, we introduce a global early termination condition that optimally balances accuracy and efficiency. Finally, a weighted singular value decomposition estimates the registration solution. Evaluations on public datasets demonstrate our algorithm achieves superior time efficiency and at least a 10% root mean square error improvement over state-of-the-art methods. The C++ source code is publicly available at https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration.
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