基于局部一致性提升点云配准的鲁棒性与精度
A Robust Probability-based Joint Registration Method of Multiple Point Clouds Considering Local Consistency
- 将局部一致性融入概率配准优化，增强对应关系可靠性
- 在含噪声和异常值条件下仍保持高精度配准结果
- 适合需要高鲁棒性的机器人检测场景使用
在机器人检测中，多点云联合配准是估计测量部件间变换关系的关键技术，如螺旋桨多个叶片间的相对位置。然而，数据中的噪声和异常值会因影响对应关系的正确性而严重损害配准性能。为此，本文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，每个测量点集被视为未知高斯混合模型（GMM）的一个样本，配准问题被建模为估计概率模型。通过在优化过程中引入局部一致性，增强了后验分布的鲁棒性和准确性，这些后验分布直接决定了一对多对应关系及最终配准结果。采用期望最大化（EM）算法推导出变换参数与概率参数的有效闭式解。大量实验表明，该方法在存在噪声和异常值的情况下仍优于现有方法，实现了高精度与强鲁棒性。代码将公开于 https://github.com/sulingjie/JPRLC_registration。
原文摘要 · Abstract (English)
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.
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