Corpus ID: 212675264

AirSim Drone Racing Lab

@article{Madaan2020AirSimDR,
  title={AirSim Drone Racing Lab},
  author={Ratnesh Madaan and Nicholas Gyde and Sai Vemprala and Matthew Brown and Keiko Nagami and Tim Taubner and Eric Cristofalo and Davide Scaramuzza and Mac Schwager and Ashish Kapoor},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.05654}
}
  • Ratnesh Madaan, Nicholas Gyde, +7 authors Ashish Kapoor
  • Published 2020
  • Engineering, Computer Science
  • 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… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 39 REFERENCES

    Deep Drone Racing: From Simulation to Reality With Domain Randomization

    VIEW 3 EXCERPTS

    Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

    VIEW 1 EXCERPT

    Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

    VIEW 1 EXCERPT

    A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

    VIEW 1 EXCERPT

    FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality