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.

翻译：水下能见度极具挑战性，且随目标与相机距离增大而恶化，这使得前向视角下的视觉任务更加困难。我们在地中海和红海收集了水下前向立体视觉和视觉-惯性图像集。据我们所知，目前尚无其他公开的采用此相机-传感器方向采集的含真值水下环境数据集。这些数据集对于开发多种水下应用至关重要，包括障碍物规避、视觉里程计、三维追踪、同步定位与地图构建（SLAM）以及深度估计。立体数据集包含动态水下环境中带已知尺寸物体的同步立体图像。视觉-惯性数据集包含单目图像和IMU测量值，数据间对齐精度达毫秒级时间戳，场景中放置了已知尺寸的物体。两种传感器配置均可实现尺度估计——立体设置利用标定基线，视觉-惯性设置利用IMU。两类数据集的真值深度图均通过摄影测量法离线生成，并通过遍布成像环境的多个已知测量结果进行验证。数据集总计包含5个立体数据集和8个视觉-惯性数据集，每个数据集包含数千张图像，涵盖不同水下能见度、环境光照条件、自然与人造结构以及动态相机运动模式。相机的前向拍摄方向使这些数据集具有独特性，特别适用于测试水下障碍物规避算法以及在动态环境中近海底导航。我们希望通过这些数据集，推动水下机器人在动态和/或浅水环境中的自主功能发展。