  Recently non-linear control methods like Model Predictive Control (MPC) and
Reinforcement Learning (RL) have attracted increased interest in the quadrotor
control community. In contrast to classic control methods like cascaded PID
controllers, MPC and RL heavily rely on an accurate model of the system
dynamics. The process of quadrotor system identification is notoriously tedious
and is often pursued with additional equipment like a thrust stand.
Furthermore, low-level details like motor delays which are crucial for accurate
end-to-end control are often neglected. In this work, we introduce a
data-driven method to identify a quadrotor's inertia parameters, thrust curves,
torque coefficients, and first-order motor delay purely based on proprioceptive
data. The estimation of the motor delay is particularly challenging as usually,
the RPMs can not be measured. We derive a Maximum A Posteriori (MAP)-based
method to estimate the latent time constant. Our approach only requires about a
minute of flying data that can be collected without any additional equipment
and usually consists of three simple maneuvers. Experimental results
demonstrate the ability of our method to accurately recover the parameters of
multiple quadrotors. It also facilitates the deployment of RL-based, end-to-end
quadrotor control of a large quadrotor under harsh, outdoor conditions.
