Abstract
Underwater exploration and related research face significant challenges, including incomplete data collections and the high cost of operations. An underwater robot's capacity to independently identify, target, and address gaps in 3D surface data, termed as ‘holes’, during a mission has the potential to significantly lower the expenses associated with research endeavors, such as those in underwater archaeology or subsea monitoring, and to improve sea surface mapping for autonomous robots. This study introduces a novel approach that combines the boundary-edge of a triangle mesh with Principal Component Analysis (PCA) to actively gather incomplete data. We propose two distinct techniques for active hole-filling, as well as a method to determine the most appropriate strategy. We introduce the flying-over and following-rail approaches to automatically determine new sensor positions for collecting additional point cloud data, which are then used to reconstruct the surface and fill the gaps. Experiments were conducted using both simulated underwater images with ORB-SLAM3 (Campos et al. (2021)) and real-world Multibeam Echo Sounder data.