论文标题
Airsim无人机赛车实验室
AirSim Drone Racing Lab
论文作者
论文摘要
在计算机视觉，规划，州估计和控制的交汇处，自动无人机赛车是一个具有挑战性的研究问题。我们介绍了Airsim无人机赛车实验室，这是一个模拟框架，用于在该领域中快速进行算法的原型制作，并启用机器学习研究，目的是减少与现场机器人技术相关的时间，金钱和风险。我们的框架使在多种逼真的环境中生成赛车轨道，无人机种族的编排，配备一套门资产，允许多种传感器方式（单眼，深度，深度，神经形态事件，光流），不同的摄像头模型以及计划，计算机视觉，控制，控制，基于学习的算法的基准测试。我们使用框架在2019年Neurips举办了基于模拟的无人机赛车比赛。竞争二进制文件可在我们的GitHub存储库中找到。
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.
