A hierarchical framework employing voting mechanism for large-scale ALB and MBES point cloud registration

Abstract

Registration is essential for fusing airborne LiDAR bathymetry (ALB) and multibeam echo sounder (MBES) data. However, existing studies are limited, often overlooking the unique characteristics and heterogeneity of bathymetric point clouds, which complicates the establishment of accurate correspondences. This study thoroughly analyzes the key challenges in ALB and MBES point cloud registration and proposes a hierarchical framework based on a voting mechanism. The framework transforms coarse registration into a hierarchical matching process, progressing from global to local compatibility graphs. It employs weak and strong voting mechanisms to progressively refine inlier selection from relaxed to strict criteria. An ICP algorithm incorporating geometric consistency is then applied to achieve fine registration. The strong voting mechanism enhances the weak voting mechanism by integrating local information. These hierarchical and voting strategies effectively reduce graph sensitivity, balancing accuracy and efficiency. Additionally, the study adapts initial correspondence extraction based on terrain complexity and introduces adaptive inlier selection through Gaussian fitting of fusion scores, calculated using the coefficient of variation. This study validates the proposed method using two field datasets from the South China Sea, providing a comprehensive analysis of the key steps in the algorithm. The proposed method outperforms Comparison Methods 1 and 2, reducing overall error (OE) by 221.2% and 206.4% on Data I, and 223.1% and 202.6% on Data II. Furthermore, experiments demonstrate the method’s robustness in complex noisy environments.