mlsanityData to 7 Oct 2026

AlphaPilot: Autonomous Drone Racing

arXiv 2005.12813 ↗First posted 26 May 2020cs.RO cs.CV cs.SYcited by 23

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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.

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

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Citing papers per year: 2021: 9; 2022: 2; 2023: 3; 2024: 2; 2025: 7.

23 papers.

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  1. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
  2. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 51
  3. 2021A Survey on Human-aware Robot Navigationcited by 23
  4. 2021EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Followingcited by 17
  5. 2021Policy Search for Model Predictive Control with Application to Agile Drone Flightcited by 17
  6. 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 14
  7. 2022Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racingcited by 13
  8. 2021The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competitioncited by 6
  9. 2022Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agentscited by 6
  10. 2021AutoTune: Controller Tuning for High-Speed Flightcited by 5
  11. 2023BatMobility: Towards Flying Without Seeing for Autonomous Dronescited by 4
  12. 2021CNN-based Ego-Motion Estimation for Fast MAV Maneuverscited by 3
11 more
  1. 2025A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehiclescited by 3
  2. 2024Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Dronescited by 2
  3. 2023Secure Deep Learning-based Distributed Intelligence on Pocket-sized Dronescited by 1
  4. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principlescited by 1
  5. 2021A simple vision-based navigation and control strategy for autonomous drone racingcited by 0
  6. 2024Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racingcited by 0
  7. 2025VizFlyt: Perception-centric Pedagogical Framework For Autonomous Aerial Robotscited by 0
  8. 2025Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handlingcited by 0
  9. 2025Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVscited by 0
  10. 2025SPIRAL: Self-Play Incremental Racing Algorithm for Learning in Multi-Drone Competitionscited by 0
  11. 2025Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduitcited by 0