用GPU加速的水下机器人感知仿真框架，提升图像与声呐模拟精度。
OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
- 基于物理的渲染技术，减少仿真与现实差距
- 支持实时声呐成像与快速数据生成
- 开源代码助力海洋机器人研究
水下仿真器为构建鲁棒的水下感知系统提供支持。尽管近期已有大量工作推进新仿真器开发及现有仿真器性能提升，但在基于物理的水下传感器建模与渲染效率方面仍有改进空间。本文提出OceanSim，一个高保真度、基于GPU加速的水下仿真框架，以填补这一研究空白。通过先进的物理基础渲染技术，有效降低水下图像仿真的‘仿真到现实’差距。OceanSim充分利用GPU计算优势，实现实时成像声呐渲染与快速合成数据生成。我们使用真实世界数据评估了OceanSim的能力与真实性，提供定性与定量结果。项目代码与详细文档已公开，网址：https://umfieldrobotics.github.io/OceanSim，以支持海洋机器人社区。
原文摘要 · Abstract (English)
Underwater simulators offer support for building robust underwater perception solutions. Significant work has recently been done to develop new simulators and to advance the performance of existing underwater simulators. Still, there remains room for improvement on physics-based underwater sensor modeling and rendering efficiency. In this paper, we propose OceanSim, a high-fidelity GPU-accelerated underwater simulator to address this research gap. We propose advanced physics-based rendering techniques to reduce the sim-to-real gap for underwater image simulation. We develop OceanSim to fully leverage the computing advantages of GPUs and achieve real-time imaging sonar rendering and fast synthetic data generation. We evaluate the capabilities and realism of OceanSim using real-world data to provide qualitative and quantitative results. The code and detailed documentation are made available on the project website to support the marine robotics community: https://umfieldrobotics.github.io/OceanSim.
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