提出真实3D声呐仿真框架，解决传统方法忽略声学效应的问题。
Towards Realistic 3D Sonar Simulation
- 结合图形引擎与物理声传播模型，实现声呐信号真实生成
- 在NVIDIA Isaac Sim中构建水下3D声呐模型并验证系统性能
- 适合水下机器人感知与导航算法研发人员使用
随着水下机器人研究日益聚焦复杂三维感知与自主导航，声呐仿真的保真度成为算法开发的关键。现有仿真框架多依赖几何渲染，将3D声呐类比于水下激光雷达，忽略了折射、多路径干扰和相位相关信号形成等基本声学现象。本文提出一种模块化真实3D声呐仿真架构，融合GPU加速图形引擎与物理驱动的声波传播原理。我们在NVIDIA Isaac Sim环境中实现基于Water Linked 3D-15传感器的体素化3D声呐模型，并集成至完整的水下仿真系统。通过硬件在环配置验证：在NVIDIA Jetson Orin Nano上运行改进版FastLIO2 SLAM，融合合成3D声呐、DVL、IMU与压力数据。最后，对比模拟输出与港湾桩基检测的真实数据，定性分析当前仿真到现实的差距，为全声学驱动的体素感知提供发展路线图。
原文摘要 · Abstract (English)
As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a key factor in algorithm development. Current simulation frameworks typically rely on geometry-driven rendering, approximating 3D sonar as an underwater equivalent to LiDAR, which fails to account for fundamental acoustic phenomena such as refraction, multi-path interference, and phase-dependent signal formation. This paper proposes a modular architecture for realistic 3D sonar simulation that integrates GPU-accelerated graphics engines with physically grounded acoustic propagation principles. We implement a volumetric 3D sonar model within the NVIDIA Isaac Sim environment, modeled after the Water Linked 3D-15 sensor, and integrate it into a comprehensive underwater simulation framework. The system is validated through a hardware-in-the-loop configuration, where a modified FastLIO2 SLAM pipeline, executed on an NVIDIA Jetson Orin Nano, performs sensor fusion using synthetic 3D sonar, DVL, IMU, and pressure data. Finally, a qualitative comparison between simulated outputs and real-world data from harbor sheet-pile inspections is provided, characterizing the remaining sim-to-real gap and establishing a roadmap toward fully acoustics-driven volumetric sensing.
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