Robust Point Cloud Registration for Aircraft Engine Pipeline Systems

TL;DR

This paper introduces a new method for accurately aligning 3D scans of aircraft engine pipelines, even when parts of the scans are missing.

Contribution

A novel registration framework with a new 3D descriptor (PL-PPFs) tailored for aircraft pipeline point cloud alignment.

Findings

The proposed framework effectively handles occlusions and similar structures in aircraft pipeline scans.

The PL-PPFs descriptor improves the accuracy of identifying pipeline structures in 3D point clouds.

Experimental results confirm the method's effectiveness on real aircraft engine pipeline data.

Abstract

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

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