ROCVSep 16, 2023

OmniLRS: A Photorealistic Simulator for Lunar Robotics

arXiv:2309.08997v121 citationsh-index: 7Has Code
Originality Synthesis-oriented
AI Analysis

This work addresses the need for quality simulators to develop and test lunar robotics algorithms, particularly for researchers and engineers in space exploration, though it is incremental as it builds on existing simulation platforms.

The authors tackled the challenge of evaluating algorithms for lunar robotic exploration by developing OmniLRS, a photorealistic simulator based on Nvidia's Isaac Sim, which includes procedural environment generation and synthetic data pipelines. They demonstrated its effectiveness through sim-to-real rock instance segmentation, where a model trained on synthetic data achieved performance close to real-world data (5% gap) and, when fine-tuned, outperformed it by 14% in average precision.

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.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes