SubPipe：用于分割和视觉惯性定位的潜艇管道检测数据集
SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization
AI总结：
本文提出SubPipe，首个带标注的水下管道检测数据集，包含SLAM、目标检测和分割任务，并基准测试了现有方法，为计算机视觉算法在水下场景的应用提供了挑战与机遇。
摘要：
本文介绍了SubPipe，一个用于SLAM、目标检测和图像分割的水下数据集。SubPipe由OceanScan MST操作的LAUV记录，搭载了包括两个摄像头、侧扫声纳和惯性导航系统在内的传感器套件。该AUV部署在管道检测环境中，其中有一条部分被沙子覆盖的潜艇管道。AUV的位姿真值由导航传感器估计。侧扫声纳和RGB图像分别包含目标检测和分割标注。在SubPipe上对最先进的分割、目标检测和SLAM方法进行了基准测试，以展示数据集在利用计算机视觉算法方面的挑战和机遇。据作者所知，这是第一个提供真实管道检测场景的带标注水下数据集。该数据集和实验可在https://github.com/remaro-network/SubPipe-dataset在线公开获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
