  This paper addresses real-time dense 3D reconstruction for a
resource-constrained Autonomous Underwater Vehicle (AUV). Underwater
vision-guided operations are among the most challenging as they combine 3D
motion in the presence of external forces, limited visibility, and absence of
global positioning. Obstacle avoidance and effective path planning require
online dense reconstructions of the environment. Autonomous operation is
central to environmental monitoring, marine archaeology, resource utilization,
and underwater cave exploration. To address this problem, we propose to use
SVIn2, a robust VIO method, together with a real-time 3D reconstruction
pipeline. We provide extensive evaluation on four challenging underwater
datasets. Our pipeline produces comparable reconstruction with that of COLMAP,
the state-of-the-art offline 3D reconstruction method, at high frame rates on a
single CPU.
