# AirSim Drone Racing Lab

arXiv: https://arxiv.org/abs/2003.05654 · HTML: https://mlsanity.com/paper/2003.05654/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 18 of 18)

- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Causal Navigation by Continuous-time Neural Networks](https://mlsanity.com/paper/2106.08314/) (2021)
- [Time-Optimal Planning for Quadrotor Waypoint Flight](https://mlsanity.com/paper/2108.04537/) (2021)
- [Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing](https://mlsanity.com/paper/2210.14985/) (2022)
- [Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents](https://mlsanity.com/paper/2201.02569/) (2022)
- [VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight](https://mlsanity.com/paper/2407.14783/) (2024)
- [AutoTune: Controller Tuning for High-Speed Flight](https://mlsanity.com/paper/2103.10698/) (2021)
- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning](https://mlsanity.com/paper/2406.19646/) (2024)
- [SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks](https://mlsanity.com/paper/2408.15200/) (2024)
- [Augmenting Imitation Experience via Equivariant Representations](https://mlsanity.com/paper/2110.07668/) (2021)
- [Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy](https://mlsanity.com/paper/2211.05335/) (2022)
- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [CERiL: Continuous Event-based Reinforcement Learning](https://mlsanity.com/paper/2302.07667/) (2023)
- [NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation](https://mlsanity.com/paper/2406.13086/) (2024)
- [NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks](https://mlsanity.com/paper/2407.01563/) (2024)
- [FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy](https://mlsanity.com/paper/2502.05038/) (2025)

## Abstract (arXiv)

Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics. Our framework enables generation of racing tracks in multiple photo-realistic environments, orchestration of drone races, comes with a suite of gate assets, allows for multiple sensor modalities (monocular, depth, neuromorphic events, optical flow), different camera models, and benchmarking of planning, control, computer vision, and learning-based algorithms. We used our framework to host a simulation based drone racing competition at NeurIPS 2019. The competition binaries are available at our github repository.