# AlphaPilot: Autonomous Drone Racing

arXiv: https://arxiv.org/abs/2005.12813 · HTML: https://mlsanity.com/paper/2005.12813/

## Key facts

- Cited by 23 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 23 of 23)

- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Policy Search for Model Predictive Control with Application to Agile Drone Flight](https://mlsanity.com/paper/2112.03850/) (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)
- [The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition](https://mlsanity.com/paper/2109.14985/) (2021)
- [Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents](https://mlsanity.com/paper/2201.02569/) (2022)
- [AutoTune: Controller Tuning for High-Speed Flight](https://mlsanity.com/paper/2103.10698/) (2021)
- [BatMobility: Towards Flying Without Seeing for Autonomous Drones](https://mlsanity.com/paper/2307.11518/) (2023)
- [CNN-based Ego-Motion Estimation for Fast MAV Maneuvers](https://mlsanity.com/paper/2101.01841/) (2021)
- [A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles](https://mlsanity.com/paper/2503.05251/) (2025)
- [Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones](https://mlsanity.com/paper/2407.02405/) (2024)
- [Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones](https://mlsanity.com/paper/2307.01559/) (2023)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [A simple vision-based navigation and control strategy for autonomous drone racing](https://mlsanity.com/paper/2104.09815/) (2021)
- [Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing](https://mlsanity.com/paper/2405.01054/) (2024)
- [VizFlyt: Perception-centric Pedagogical Framework For Autonomous Aerial Robots](https://mlsanity.com/paper/2503.22876/) (2025)
- [Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling](https://mlsanity.com/paper/2504.21695/) (2025)
- [Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs](https://mlsanity.com/paper/2510.16624/) (2025)
- [SPIRAL: Self-Play Incremental Racing Algorithm for Learning in Multi-Drone Competitions](https://mlsanity.com/paper/2510.22568/) (2025)
- [Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit](https://mlsanity.com/paper/2512.10934/) (2025)

## Abstract (arXiv)

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to 8m/s and ranked second at the 2019 AlphaPilot Challenge.