Multi-modal Maritime Perception Dataset

This paper introduces the first publicly accessible labeled 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, collected over 4 years and consisting of diverse objects 
encountered under varying environmental conditions, aims to bridge the research gap 
in autonomous surface vehicles 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 by training deep learning-based 
open-source perception algorithms that have shown success. We expect that our dataset 
will contribute to development of the marine autonomy pipelines 
and marine (field) robotics.