  We demonstrate the capabilities of an attention-based end-to-end approach for
high-speed quadrotor obstacle avoidance in dense, cluttered environments, with
comparison to various state-of-the-art architectures. Quadrotor unmanned aerial
vehicles (UAVs) have tremendous maneuverability when flown fast; however, as
flight speed increases, traditional vision-based navigation via independent
mapping, planning, and control modules breaks down due to increased sensor
noise, compounding errors, and increased processing latency. Thus,
learning-based, end-to-end planning and control networks have shown to be
effective for online control of these fast robots through cluttered
environments. We train and compare convolutional, U-Net, and recurrent
architectures against vision transformer models for depth-based end-to-end
control, in a photorealistic, high-physics-fidelity simulator as well as in
hardware, and observe that the attention-based models are more effective as
quadrotor speeds increase, while recurrent models with many layers provide
smoother commands at lower speeds. To the best of our knowledge, this is the
first work to utilize vision transformers for end-to-end vision-based quadrotor
control.
