Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement

2025-02-11

IEEE Transactions on Instrumentation and Measurement

AI Summary

In industrial metrology, global registration of multi-view point clouds from freeform turbine blades suffers from low accuracy due to severe noise and substantial data incompleteness. To address this, this paper proposes a novel Minimum Potential Energy (MPE)-based registration method. It innovatively introduces a physical potential energy model into point cloud registration, formulating a weighted MPE optimization objective. A dual-flag mechanism is designed to dynamically assess registration status, while a coarse-to-fine strategy enhances robustness and convergence. Furthermore, a force-guided operator and an improved TrICP algorithm are introduced. Experiments on four real-world blade datasets demonstrate that the proposed method achieves higher registration accuracy and superior noise resilience compared to state-of-the-art global registration approaches, significantly improving the reliability and practicality of industrial-grade freeform surface reconstruction.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3-D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this article, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The basic strategy is that the objective function is defined as the MPE optimization function of the physical registration system. The function distributes more weight to the majority of inlier points and less weight to the noise and outliers, which essentially reduces the influence of perturbations in the mathematical formulation. We decompose the solution into a globally optimal approximation procedure and a fine registration process with the trimmed iterative closest point algorithm to boost convergence. The approximation procedure consists of two main steps. First, according to the construction of the force traction operator, we can simply compute the position of the potential energy minimum. Second, to find the MPE point, we propose a new theory that employs two flags to observe the status of the registration procedure. We demonstrate the performance of the proposed algorithm on four types of blades. The proposed method outperforms the other global methods in terms of both accuracy and noise resistance.

Problem

• Multiview point cloud registration
• Noisy and incomplete data
• Minimum potential energy method

Innovation

• Minimum potential energy registration
• Trimmed iterative closest point
• Global and fine registration decomposition