# UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework

arXiv: https://arxiv.org/abs/2310.11927 · HTML: https://mlsanity.com/paper/2310.11927/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2023.

## Built on by (most cited first, 17 of 17)

- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Stonefish: Supporting Machine Learning Research in Marine Robotics](https://mlsanity.com/paper/2502.11887/) (2025)
- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics](https://mlsanity.com/paper/2503.09203/) (2025)
- [Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles](https://mlsanity.com/paper/2410.00120/) (2024)
- [Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges](https://mlsanity.com/paper/2412.11840/) (2024)
- [MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation](https://mlsanity.com/paper/2403.09850/) (2024)
- [Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison](https://mlsanity.com/paper/2502.20154/) (2025)
- [Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models](https://mlsanity.com/paper/2503.06784/) (2025)
- [ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research](https://mlsanity.com/paper/2506.22174/) (2025)
- [MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation](https://mlsanity.com/paper/2410.14117/) (2024)
- [EREBUS: End-to-end Robust Event Based Underwater Simulation](https://mlsanity.com/paper/2511.01381/) (2025)
- [Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control](https://mlsanity.com/paper/2504.20019/) (2025)
- [Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models](https://mlsanity.com/paper/2507.02148/) (2025)
- [UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow](https://mlsanity.com/paper/2601.10054/) (2026)
- [Towards Scaling Marine Perception with Synthetic Data](https://mlsanity.com/paper/2609.20680/) (2026)
- [A Unified Dynamics Framework for Reinforcement Learning and Classical Control of a Six-DOF Pipeline-Tracking ROV in NVIDIA Isaac Sim](https://mlsanity.com/paper/2610.04949/) (2026)

## Abstract (arXiv)

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently.