Efficient Large-scale AUV-based Visual Seafloor Mapping
Mengkun She Yifan Song David Nakath Kevin Köser
Driven by the increasing number of marine data science applications, there is
a growing interest in surveying and exploring the vast, uncharted terrain of
the deep sea with robotic platforms. Despite impressive results achieved by
many on-land visual mapping algorithms in the past decades, transferring these
methods from land to the deep sea remains a challenge due to harsh
environmental conditions. Typically, deep-sea exploration involves the use of
autonomous underwater vehicles (AUVs) equipped with high-resolution cameras and
artificial illumination systems. However, images obtained in this manner often
suffer from heterogeneous illumination and quality degradation due to
attenuation and scattering, on top of refraction of light rays. All of this
together often lets on-land SLAM approaches fail underwater or makes
Structure-from-Motion approaches drift or omit difficult images, resulting in
gaps, jumps or weakly registered areas. In this work, we present a system that
incorporates recent developments in underwater imaging and visual mapping to
facilitate automated robotic 3D reconstruction of hectares of seafloor. Our
approach is efficient in that it detects and reconsiders difficult, weakly
registered areas, to avoid omitting images and to make better use of limited
dive time; on the other hand it is computationally efficient; leveraging a
hybrid approach combining benefits from SLAM and Structure-from-Motion that
runs much faster than incremental reconstructions while achieving at least
on-par performance. The proposed system has been extensively tested and
evaluated during several research cruises, demonstrating its robustness and
practicality in real-world conditions.