  This paper presents SubPipe, an underwater dataset for SLAM, object
detection, and image segmentation. SubPipe has been recorded using a
\gls{LAUV}, operated by OceanScan MST, and carrying a sensor suite including
two cameras, a side-scan sonar, and an inertial navigation system, among other
sensors. The AUV has been deployed in a pipeline inspection environment with a
submarine pipe partially covered by sand. The AUV's pose ground truth is
estimated from the navigation sensors. The side-scan sonar and RGB images
include object detection and segmentation annotations, respectively.
State-of-the-art segmentation, object detection, and SLAM methods are
benchmarked on SubPipe to demonstrate the dataset's challenges and
opportunities for leveraging computer vision algorithms. To the authors'
knowledge, this is the first annotated underwater dataset providing a real
pipeline inspection scenario. The dataset and experiments are publicly
available online at https://github.com/remaro-network/SubPipe-dataset
