A Robust Probability-based Joint Registration Method of Multiple Point Clouds Considering Local Consistency

TL;DR

This paper introduces a robust joint registration method for multiple point clouds that incorporates local consistency and Gaussian Mixture Models, improving accuracy and robustness in noisy and outlier-prone data.

Contribution

It proposes a novel probability-based registration approach that integrates local consistency, with an EM algorithm for closed-form solutions, outperforming existing methods.

Findings

Outperforms existing registration methods in accuracy.

Demonstrates robustness against noise and outliers.

Provides effective closed-form solutions for registration parameters.

Abstract

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.