基于声纳的水下机器人深度学习：概述、鲁棒性与挑战
Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges
AI总结：
本文首次全面综述水下机器人中基于声纳的深度学习，聚焦鲁棒性挑战，涵盖感知任务、数据集与鲁棒性方法，并提出建立基线数据集及弥合模拟-现实差距的未来方向。
摘要：
随着对水下探索与监测兴趣的增长，自主水下机器人（AUVs）已变得至关重要。近期对机载深度学习（DL）的兴趣推动了依赖高效且准确的基于视觉的深度学习模型的实时环境交互能力的发展。然而，水下环境中声纳的主导使用，其特点是训练数据有限且固有噪声，对模型鲁棒性构成挑战。这种自主性的提升引发了在水下作业期间部署此类模型的安全担忧，可能导致危险情况。本文旨在提供首个在鲁棒性范畴下基于声纳的深度学习的全面概述。它研究了基于声纳的深度学习感知任务模型，如分类、目标检测、分割和SLAM。此外，本文系统化了基于声纳的最新数据集、模拟器以及鲁棒性方法，如神经网络验证、分布外和对抗性攻击。本文强调了基于声纳的深度学习研究中鲁棒性的缺乏，并提出了未来研究路径，特别是建立基线声纳数据集和弥合模拟到现实的差距。
英文摘要：
With the growing interest in underwater exploration and monitoring, Autonomous Underwater Vehicles (AUVs) have become essential. The recent interest in onboard Deep Learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This paper aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and SLAM. Furthermore, the paper systematizes sonar-based state-of-the-art datasets, simulators, and robustness methods such as neural network verification, out-of-distribution, and adversarial attacks. This paper highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based dataset and bridging the simulation-to-reality gap.
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