升级版仿真平台石鱼，助力水下机器人机器学习研究。
Stonefish: Supporting Machine Learning Research in Marine Robotics
- 新增事件相机等多类传感器，提升仿真真实度。
- 支持有线作业与更精准声呐，适配复杂水下场景。
- 提供自动标注工具，解决机器学习数据难题。
仿真在海洋机器人领域极具价值，能以低成本、可控方式测试水下及水面操作。由于实地试验成本高、部署难，能够模拟海底环境运行条件的仿真器成为研发遥控与自主水下航行器算法的关键。本文介绍石鱼（Stonefish）仿真平台的最新改进，该开源平台支持海洋机器人方案开发与测试。主要更新包括新增事件相机、热成像相机、光学流相机，支持可见光通信与有缆作业，改进推进器建模，增强水动力灵活性，并提升声呐精度。此外，新增自动化标注工具，显著强化其在机器学习研究中的作用，尤其解决了难以获取带真实标签训练数据的问题。
原文摘要 · Abstract (English)
Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect.
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