水下机器人通过视觉实现类无人机级自主探索与检测
Ariel Explores: Vision-based underwater exploration and inspection via generalist drone-level autonomy
- 基于多相机与惯性传感器，融合折射补偿与自学习速度预测
- 在特隆赫姆潜艇干坞复杂环境下验证状态估计鲁棒性
- 适用于多种机器人本体，适合水下巡检与勘探场景
本文提出一种集成于定制视觉驱动水下机器人Ariel的视觉感知自主探索与检测方案。Ariel配备5个摄像头和惯性测量单元（IMU）传感套件，采用考虑折射效应的多相机视觉-惯性状态估计算法，并结合基于学习的本体感知速度预测方法，提升在视觉退化条件下的鲁棒性。同时，将此前已广泛实地验证的自主探索与通用视觉检测系统部署于Ariel，实现水下类无人机级自主能力。系统在挪威特隆赫姆潜艇干坞中进行了实地测试，面对挑战性视觉环境，验证了状态估计的稳健性及路径规划技术在不同机器人本体间的泛化能力。
原文摘要 · Abstract (English)
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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