Ariel Explores：基于视觉的水下探索与检测及通用无人机级自主能力
Ariel Explores: Vision-based underwater exploration and inspection via generalist drone-level autonomy
AI总结：
本工作将基于视觉的水下探索与检测自主解决方案集成到水下机器人Ariel中，通过多相机视觉惯性状态估计与本体感受速度预测增强鲁棒性，并在潜艇干船坞实地验证了其无人机级自主能力及跨本体路径规划的通用性。
摘要：
本工作提出了一种基于视觉的水下探索与检测自主解决方案，并集成到定制视觉驱动的水下机器人Ariel中。Ariel搭载5个相机及基于IMU的传感套件，支持折射感知的多相机视觉惯性状态估计方法，该方法由基于学习的本体感受机器人速度预测方法辅助，增强了对视觉退化的鲁棒性。此外，我们将先前开发并经广泛实地验证的自主探索与通用视觉检测方案集成到Ariel上，提供水下航空无人机级自主能力。所提系统在Trondheim的潜艇干船坞中于挑战性视觉条件下进行了实地测试。实地演示展示了状态估计解决方案的鲁棒性及路径规划技术在不同机器人本体间的通用性。
英文摘要：
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.
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