AMP2026: A Multi-Platform Marine Robotics Dataset for Tracking and Mapping

Edwin Meriaux and Shuo Wen and David Widhalm and Zhizun Wang and Junming Shi and Mariana Sosa Guzmán and Kalvik Jakkala and Bennett A. Carley and Elias Sokolova and Yogesh Girdhar and Monika Roznere and Jason M. O'Kane and Junaed Sattar and Gregory Dudek
In Proc. Conference on Robots and Vision
2026
To appear

Abstract Marine environments present significant challenges for perception and autonomy due to dynamic surfaces, limited visibility, and complex interactions between aerial, surface, and submerged sensing modalities. This paper introduces the Aerial-Marine Perception Dataset (AMP2026), a multi-platform marine robotics dataset collected across multiple field deployments designed to support research in two primary areas: multi-view tracking and marine environment mapping. The dataset includes synchronized data from aerial drones, boat-mounted cameras, and submerged robotic platforms, along with associated localization and telemetry information when available. The goal of this work is to provide a publicly available dataset enabling research in marine perception and multi-robot observation scenarios. This paper describes the data collection methodology, sensor configurations, dataset organization, and intended research tasks supported by the dataset.

@inproceedings{MerWen+26,
  author = { Edwin Meriaux and Shuo Wen and David Widhalm and Zhizun
            Wang and Junming Shi and Mariana Sosa Guzm\'{a}n
            and Kalvik Jakkala and Bennett A. Carley and
            Elias Sokolova and Yogesh Girdhar and Monika
            Roznere and Jason M. O'Kane and Junaed Sattar
            and Gregory Dudek},
  booktitle = {Proc. Conference on Robots and Vision},
  note = {To appear},
  title = {AMP2026: A Multi-Platform Marine Robotics Dataset for
           Tracking and Mapping},
  year = {2026}
}


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