面向真实3D声纳仿真
Towards Realistic 3D Sonar Simulation
AI总结：
本文提出一种模块化架构，结合GPU加速图形引擎与物理声学传播原理，在NVIDIA Isaac Sim中实现基于Water Linked 3D-15传感器的体积3D声纳模型，并通过硬件在环配置验证其有效性。
摘要：
随着水下机器人研究日益涉及复杂的三维感知和自主导航，声纳仿真的保真度已成为算法开发的关键因素。当前的仿真框架通常依赖于几何驱动的渲染，将3D声纳近似为水下的LiDAR等效物，这未能考虑基本的声学现象，如折射、多径干扰和相位相关的信号形成。本文提出了一种用于真实3D声纳仿真的模块化架构，该架构将GPU加速的图形引擎与基于物理的声学传播原理相结合。我们在NVIDIA Isaac Sim环境中实现了一个体积3D声纳模型，该模型以Water Linked 3D-15传感器为原型，并将其集成到一个全面的水下仿真框架中。该系统通过硬件在环配置进行了验证，其中在NVIDIA Jetson Orin Nano上执行的改进FastLIO2 SLAM流水线使用合成3D声纳、DVL、IMU和压力数据进行传感器融合。最后，提供了模拟输出与来自港口板桩检查的真实数据之间的定性比较，描述了剩余的模拟到现实差距，并建立了迈向完全声学驱动的体积感知的路线图。
英文摘要：
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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