Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles

• URL: http://arxiv.org/abs/2404.18411v1
• Date: Mon, 29 Apr 2024 04:00:19 GMT
• Title: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
• Authors: Mingi Jeong, Arihant Chadda, Ziang Ren, Luyang Zhao, Haowen Liu, Monika Roznere, Aiwei Zhang, Yitao Jiang, Sabriel Achong, Samuel Lensgraf, Alberto Quattrini Li,
• Abstract summary: This paper introduces the first publicly accessible multi-modal perception dataset for autonomous maritime navigation. It focuses on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs)
• Score: 10.732732686425308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the first publicly accessible multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs). This dataset, consisting of diverse objects encountered under varying environmental conditions, aims to bridge the research gap in marine robotics by providing a multi-modal, annotated, and ego-centric perception dataset, for object detection and classification. We also show the applicability of the proposed dataset's framework using deep learning-based open-source perception algorithms that have shown success. We expect that our dataset will contribute to development of the marine autonomy pipeline and marine (field) robotics. Please note this is a work-in-progress paper about our on-going research that we plan to release in full via future publication.

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