  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.
