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

Edwin Meriaux, Shuo Wen, David Widhalm, Zhizun Wang, Junming Shi, Mariana Sosa Guzmán, Kalvik Jakkala, Bennett Carley, Elias Sokolova, Yogesh Girdhar, Monika Roznere, Jason O'Kane, Junaed Sattar, Gregory Dudek

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. 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.

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

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. 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.
Paper Structure (25 sections, 11 figures)

This paper contains 25 sections, 11 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dataset Categories
  4. Tracking
  5. Mapping of Marine Environments
  6. Data Collection
  7. Collection Sites
  8. Data Collection Platforms
  9. Aerial Drones
  10. Submerged Robotic Platform
  11. Diver-Based Data Collection
  12. Surface Vessel
  13. Test Methodology by Category
  14. Tracking Sequences
  15. Multi-Drone Tracking of Submerged Robotic Platforms
  16. ...and 10 more sections

Figures (11)

  • Figure 1: AMP2026 dataset overview organized by task category. Colored blocks correspond to where the data of a particular class was collected, while the green symbol indicates the availability of GNSS ground truth.
  • Figure 2: Distribution of data collected in AMP2026 dataset across the different categories and classes in Figure \ref{['fig:dataset_overview_blocks']}
  • Figure 3: Dataset collection locations for AMP2026.
  • Figure 4: Reefs off the coast of Bellairs Research Institute of McGill University in Barbados. This image was generated with inspiration from the work seen in turgeon2011home
  • Figure 5: Tracking Category 1: Top-down aerial image sample displaying a multi-drone tracking system in action to track two submerged vehicles (ID:1 and ID:2).
  • ...and 6 more figures