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

TL;DR

This paper introduces Ariel, a vision-based underwater robot with drone-level autonomy, featuring a refraction-aware visual-inertial state estimation and learning-based velocity prediction, demonstrated in challenging underwater environments.

Contribution

The work integrates a robust vision-based autonomy system into Ariel, enabling general underwater exploration and inspection with drone-level autonomy and proven effectiveness in real-world tests.

Findings

Robust visual-inertial state estimation under challenging conditions

Effective learning-based velocity prediction enhances robustness

Successful field tests in submarine dry dock

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.