  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.
