SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

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...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Álvarez-Tuñón, Olaya, Luiza Ribeiro Marnet, Antal, László, Aubard, Martin, Costa, Maria, Brodskiy, Yury
Format: Article
Language:English
Subjects:
Algorithms
Autonomous underwater vehicles
Color imagery
Computer vision
Datasets
Image segmentation
Inertial navigation
Inspection
Navigation systems
Object recognition
Sensors
Side scan sonar
Simultaneous localization and mapping
Underwater pipelines
Underwater structures
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Summary: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
ISSN:2331-8422