AirSim Drone Racing Lab

• URL: http://arxiv.org/abs/2003.05654v1
• Date: Thu, 12 Mar 2020 08:06:06 GMT
• Title: AirSim Drone Racing Lab
• Authors: Ratnesh Madaan, Nicholas Gyde, Sai Vemprala, Matthew Brown, Keiko Nagami, Tim Taubner, Eric Cristofalo, Davide Scaramuzza, Mac Schwager, Ashish Kapoor
• Abstract summary: AirSim Drone Racing Lab is a simulation framework for enabling machine learning research in this domain. Our framework enables generation of racing tracks in multiple photo-realistic environments. We used our framework to host a simulation based drone racing competition at NeurIPS 2019.
• Score: 56.68291351736057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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