EsaacSim：适用于NVIDIA Isaac Sim的多模态事件相机附加组件
EsaacSim: A Multimodal Event Camera Add-on for NVIDIA Isaac Sim
- Leibniz Universität Hannover（莱布尼茨汉诺威大学）
- L3S Research Center（L3S研究中心）
- University of Southern Denmark（南丹麦大学）
- University of Chile（智利大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出适用于NVIDIA Isaac Sim的多模态事件相机附加组件EsaacSim，可实现多模态事件相机在线仿真，在多种分辨率下具备高效性能，支持机器人研究的多模态仿真与合成数据生成。
AI中文摘要：
基于事件的视觉正成为机器人技术中日益重要的传感范式，但其应用仍受限于传感器的可得性以及现代机器人平台缺乏集成仿真工具的问题。本文提出了EsaacSim，一款适用于NVIDIA Isaac Sim的多模态事件相机附加组件，可实现可配置事件相机的在线仿真，支持灰度和Bayer RGGB事件生成。该框架支持多种事件相机分辨率，并通过原生ROS2接口提供同步的RGB、APS、事件、深度和IMU输出。运动引导的帧间隙合成策略进一步提升了有效时间分辨率，同时保持与Isaac Sim渲染管线的兼容性。实验评估显示，该工具在代表性机器人场景中实现了同步多模态仿真，在5种事件相机分辨率、240至960Hz的有效事件率下具备高效的在线性能。在NVIDIA RTX 4060 GPU上，灰度事件流生成需6.98至27.28ms，Bayer RGGB事件流生成需7.58至29.16ms，额外GPU内存占用不足400MB。这些结果表明，EsaacSim支持机器人研究的在线多模态事件相机仿真及合成数据生成，我们发布了该仿真器的早期版本，并报告其当前架构与性能。
英文摘要：
Event-based vision is becoming an increasingly important sensing paradigm for robotics, yet its adoption remains limited by sensor availability and the lack of integrated simulation tools for modern robotics platforms. This paper presents EsaacSim, a multimodal event camera add-on for NVIDIA Isaac Sim that enables online simulation of configurable event cameras with grayscale and Bayer RGGB event generation. The framework supports multiple event camera resolutions and provides synchronized RGB, APS, event, depth, and IMU outputs through native ROS2 interfaces. A motion-guided frame-gap synthesis strategy further increases the effective temporal resolution while preserving compatibility with the Isaac Sim rendering pipeline. Experimental evaluation demonstrates synchronized multimodal simulation across representative robotic scenes and efficient online performance over five event camera resolutions at effective event rates from 240 to 960Hz. Event stream generation requires 6.98--27.28ms for grayscale events and 7.58--29.16ms for Bayer RGGB events while using less than 400MB of additional GPU memory on an NVIDIA RTX~4060 GPU. These results show that EsaacSim enables supports online multimodal event-camera simulation for robotics research and synthetic data generation. We release an early version of the simulator and report its current architecture and performance.
