Ariel Explores: Vision-based underwater exploration and inspection via generalist drone-level autonomy

Mohit Singh, Mihir Dharmadhikari, Kostas Alexis
NTNU: Norwegian University of Science and Technology
arXiv Video

Vision-driven underwater autonomous exploration and inspection

Vision-driven underwater exploration

Abstract

This work presents a vision-based underwater exploration and inspection autonomy solution integrated into Ariel, a custom vision-driven underwater robot. Ariel carries a 5 camera and IMU based sensing suite, enabling a refraction-aware multi-camera visual-inertial state estimation method aided by a learning-based proprioceptive robot velocity prediction method that enhances robustness against visual degradation. Furthermore, our previously developed and extensively field-verified autonomous exploration and general visual inspection solution is integrated on Ariel, providing aerial drone-level autonomy underwater. The proposed system is field-tested in a submarine dry dock in Trondheim under challenging visual conditions. The field demonstration shows the robustness of the state estimation solution and the generalizability of the path planning techniques across robot embodiments.

Video

Key Methods for Vision-driven underwater exploration

ReAqROVIO: Refractive Aquatic ROVIO DeepVL: Deep Velocity Learning GB Planner: Graph Based Planner

BibTeX

@misc{singh2025arielexploresvisionbasedunderwater,
      title={Ariel Explores: Vision-based underwater exploration and inspection via generalist drone-level autonomy}, 
      author={Mohit Singh and Mihir Dharmadhikari and Kostas Alexis},
      year={2025},
      eprint={2507.10003},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2507.10003}, }