一种考虑局部一致性的鲁棒基于概率的多点云联合配准方法
A Robust Probability-based Joint Registration Method of Multiple Point Clouds Considering Local Consistency
AI总结：
针对机器人检测中多点云配准受噪声和外点干扰的问题，提出融入局部一致性的概率式联合配准方法，基于GMM建模并用EM算法求解，在噪声和外点场景下精度与鲁棒性优于现有方法。
摘要：
在机器人检测任务中，多点云联合配准是估算被测部件（如螺旋桨的多个叶片）间变换关系的核心技术。然而，数据中存在的噪声和外点会影响对应关系的正确性，严重降低配准性能。为解决该问题，我们将局部一致性特性引入基于概率的联合配准方法中。具体而言，每个被测点集都被视为来自未知高斯混合模型（Gaussian Mixture Model, GMM）的样本，配准问题被建模为对该概率模型的估计。通过在优化过程中融入局部一致性，我们提升了后验分布的鲁棒性与准确性，而后验分布代表着直接决定配准结果的“一对多”对应关系。我们利用期望最大化（Expectation-Maximization, EM）算法推导出了变换参数与概率参数的有效闭式解。大量实验表明，本文方法优于现有方法，在存在噪声和外点的情况下仍能实现高精度与强鲁棒性。代码将发布于https://github.com/sulingjie/JPRLC_registration。
英文摘要：
In robotic inspection, joint registration of multiple point clouds is an essential technique for estimating the transformation relationships between measured parts, such as multiple blades in a propeller. However, the presence of noise and outliers in the data can significantly impair the registration performance by affecting the correctness of correspondences. To address this issue, we incorporate local consistency property into the probability-based joint registration method. Specifically, each measured point set is treated as a sample from an unknown Gaussian Mixture Model (GMM), and the registration problem is framed as estimating the probability model. By incorporating local consistency into the optimization process, we enhance the robustness and accuracy of the posterior distributions, which represent the one-to-all correspondences that directly determine the registration results. Effective closed-form solution for transformation and probability parameters are derived with Expectation-Maximization (EM) algorithm. Extensive experiments demonstrate that our method outperforms the existing methods, achieving high accuracy and robustness with the existence of noise and outliers. The code will be available at https://github.com/sulingjie/JPRLC_registration.
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。
