Developing algorithms for extra-terrestrial robotic exploration has always been challenging. Along with the complexity associated with these environments, one of the main issues remains the evaluation of said algorithms. With the regained interest in lunar exploration, there is also a demand for quality simulators that will enable the development of lunar robots. % In this paper, we explain how we built a Lunar simulator based on Isaac Sim, Nvidia's robotic simulator. In this paper, we propose Omniverse Lunar Robotic-Sim (OmniLRS) that is a photorealistic Lunar simulator based on Nvidia's robotic simulator. This simulation provides fast procedural environment generation, multi-robot capabilities, along with synthetic data pipeline for machine-learning applications. It comes with ROS1 and ROS2 bindings to control not only the robots, but also the environments. This work also performs sim-to-real rock instance segmentation to show the effectiveness of our simulator for image-based perception. Trained on our synthetic data, a yolov8 model achieves performance close to a model trained on real-world data, with 5% performance gap. When finetuned with real data, the model achieves 14% higher average precision than the model trained on real-world data, demonstrating our simulator's photorealism.% to realize sim-to-real. The code is fully open-source, accessible here: https://github.com/AntoineRichard/LunarSim, and comes with demonstrations.

基于Nvidia的机器人模拟器，我们构建了一个基于Isaac Sim的月球模拟器，名为Omniverse Lunar Robotic-Sim（OmniLRS）。该模拟器提供快速的程序化环境生成、多机器人能力以及机器学习应用的合成数据管道。通过我们的合成数据进行训练，yolov8模型在实例分割方面表现接近于在真实数据上训练的模型，性能差距为5%。在使用真实数据进行微调后，该模型的平均精度比在真实世界数据上训练的模型高14%，证明了我们模拟器的真实感。

OmniLRS：月球机器人的逼真模拟器