OmniLRS: A Photorealistic Simulator for Lunar Robotics
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arXiv
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| Format: | Preprint |
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2023
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| author | Richard, Antoine Kamohara, Junnosuke Uno, Kentaro Santra, Shreya van der Meer, Dave Olivares-Mendez, Miguel Yoshida, Kazuya |
| author_facet | Richard, Antoine Kamohara, Junnosuke Uno, Kentaro Santra, Shreya van der Meer, Dave Olivares-Mendez, Miguel Yoshida, Kazuya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08997 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | OmniLRS: A Photorealistic Simulator for Lunar Robotics Richard, Antoine Kamohara, Junnosuke Uno, Kentaro Santra, Shreya van der Meer, Dave Olivares-Mendez, Miguel Yoshida, Kazuya Robotics Computer Vision and Pattern Recognition 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. |
| title | OmniLRS: A Photorealistic Simulator for Lunar Robotics |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.08997 |