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OmniLRS: A Photorealistic Simulator for Lunar Robotics
Open accessAbstract
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 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. The code is fully open-source, accessible here: https://github.com/AntoineRichard/OmniLRS, and comes with demonstrations.
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“As a result, there is a lack of space mission-based planetary datasets, standardized benchmarks, and evaluation protocols.”
From Planetary Terrain Datasets and Benchmarks for Rover Path Planning (2025) · cites this paper as Richard et al., 2024
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“The vast majority of existing datasets are typically derived from planetary-analog terrains on Earth (Vayugundla, 2022; Arm et al., 2023) or simulated environments with minimal planetary terrain fidelity such as synthetically generated (Richard et al., 2024).”
From Planetary Terrain Datasets and Benchmarks for Rover Path Planning (2025) · cites this paper as Richard et al., 2024
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References 30 · Cited by 15
Research works printed but not linked: 27
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Metadata
- Authors
- Richard, A., Kamohara, J., Uno, K., Santra, S., van der Meer, D., Olivares-Mendez, M., & Yoshida, K.
- Affiliations
- University of Luxembourg (LU)
- University of Luxembourg,The Space Robotics Research Group at the Interdisciplinary Research Center for Security Reliability and Trust (SnT)
- Venue
- 2024 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2024-05-13
- Pages
- 16901-16907
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W4401415831
- Cited by
- 15
- References
- 30
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 3
- FWCI
- 4.33
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- lunar robotics, photorealistic simulation, procedural environment generation, multi-robot simulation, sim-to-real transfer, YOLOv8, open-source simulator, lunar exploration
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- CoChat. (2026). Citation check: OmniLRS: A Photorealistic Simulator for Lunar Robotics. https://app.cochat.ai/papers/10.1109/icra57147.2024.10610026
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- Richard, A., Kamohara, J., Uno, K., Santra, S., van der Meer, D., Olivares-Mendez, M., & Yoshida, K. (2024). OmniLRS: A Photorealistic Simulator for Lunar Robotics. 2024 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra57147.2024.10610026
