  Visibility underwater is challenging, and degrades as the distance between
the subject and camera increases, making vision tasks in the forward-looking
direction more difficult. We have collected underwater forward-looking
stereo-vision and visual-inertial image sets in the Mediterranean and Red Sea.
To our knowledge there are no other public datasets in the underwater
environment acquired with this camera-sensor orientation published with
ground-truth. These datasets are critical for the development of several
underwater applications, including obstacle avoidance, visual odometry, 3D
tracking, Simultaneous Localization and Mapping (SLAM) and depth estimation.
The stereo datasets include synchronized stereo images in dynamic underwater
environments with objects of known-size. The visual-inertial datasets contain
monocular images and IMU measurements, aligned with millisecond resolution
timestamps and objects of known size which were placed in the scene. Both
sensor configurations allow for scale estimation, with the calibrated baseline
in the stereo setup and the IMU in the visual-inertial setup. Ground truth
depth maps were created offline for both dataset types using photogrammetry.
The ground truth is validated with multiple known measurements placed
throughout the imaged environment. There are 5 stereo and 8 visual-inertial
datasets in total, each containing thousands of images, with a range of
different underwater visibility and ambient light conditions, natural and
man-made structures and dynamic camera motions. The forward-looking orientation
of the camera makes these datasets unique and ideal for testing underwater
obstacle-avoidance algorithms and for navigation close to the seafloor in
dynamic environments. With our datasets, we hope to encourage the advancement
of autonomous functionality for underwater vehicles in dynamic and/or shallow
water environments.
