AirSim Drone Racing Lab
This provides a tool for researchers in robotics and AI to prototype algorithms more efficiently, though it is incremental as it builds on existing simulation concepts.
The authors tackled the challenge of autonomous drone racing by introducing AirSim Drone Racing Lab, a simulation framework that reduces time, cost, and risks in field robotics, and used it to host a competition at NeurIPS 2019.
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.