Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping
This work addresses the problem of automated robotic seafloor mapping for marine research, offering an incremental improvement over existing methods by handling harsh underwater conditions.
The paper tackles the challenge of applying on-land visual mapping algorithms to deep-sea environments by proposing a navigation-aided hierarchical reconstruction approach that combines SLAM and global SfM, resulting in robust and practical 3D reconstruction of hectares of seafloor with improved completeness and consistency.
Despite impressive results achieved by many on-land visual mapping algorithms in the recent decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Images captured by autonomous underwater vehicles (AUVs), equipped with high-resolution cameras and artificial illumination systems, often suffer from heterogeneous illumination and quality degradation caused by attenuation and scattering, on top of refraction of light rays. These challenges often result in the failure of on-land SLAM approaches when applied underwater or cause SfM approaches to exhibit drifting or omit challenging images. Consequently, this leads to gaps, jumps, or weakly reconstructed areas. In this work, we present a navigation-aided hierarchical reconstruction approach to facilitate the automated robotic 3D reconstruction of hectares of seafloor. Our hierarchical approach combines the advantages of SLAM and global SfM that is much more efficient than incremental SfM, while ensuring the completeness and consistency of the global map. This is achieved through identifying and revisiting problematic or weakly reconstructed areas, avoiding to omit images and making better use of limited dive time. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real-world conditions.