Robust pose estimation for ship block assembly feature based on large-scale scanning

Abstract

Purpose

Hull block assembly is a vital task in ship construction. It is necessary to obtain the actual poses of the assembly features to guide further block alignment. Traditional methods use single-point measurement, which is time-consuming and may lead to loss of key information. Thus, large-scale scanning is introduced for data acquisition, and this paper aims to provide a precise and robust method for retrieving poses based on point set registration.

Design/methodology/approach

The main problem of point registration is to find the correct transformation between the model and the scene. In this paper, a vote framework based on a new point pair feature is used to calculate the transformation. First, a special edge indicator for multiplate objects is proposed to determine the edges. Subsequently, pair features with an edge description are noted for every point. Finally, a voting scheme based on agglomerative clustering is implemented to determine the optimal transformation.

Findings

The proposed method not only improves registration efficiency but also maintains high accuracy compared to several commonly used approaches. In particular, for objects composed of plates, the results of pose estimation are more promising because of the compact pair feature. The multiple ship longitudinal localization experiment validates the effectiveness in real scan applications.

Originality/value

The proposed edge description performs a better detection for the edges of multiplate objects. The pair feature incorporating the edge indicator is more discriminative than the original template, resulting in better robustness to outliers, noise and occlusions.

Citation

Tang, C., Chen, G., Wang, H. and Yu, Y. (2023), "Robust pose estimation for ship block assembly feature based on large-scale scanning", Robotic Intelligence and Automation, Vol. 43 No. 4, pp. 406-419. https://doi.org/10.1108/RIA-09-2022-0239