  Inertial odometry is an attractive solution to the problem of state
estimation for agile quadrotor flight. It is inexpensive, lightweight, and it
is not affected by perceptual degradation. However, only relying on the
integration of the inertial measurements for state estimation is infeasible.
The errors and time-varying biases present in such measurements cause the
accumulation of large drift in the pose estimates. Recently, inertial odometry
has made significant progress in estimating the motion of pedestrians.
State-of-the-art algorithms rely on learning a motion prior that is typical of
humans but cannot be transferred to drones. In this work, we propose a
learning-based odometry algorithm that uses an inertial measurement unit (IMU)
as the only sensor modality for autonomous drone racing tasks. The core idea of
our system is to couple a model-based filter, driven by the inertial
measurements, with a learning-based module that has access to the thrust
measurements. We show that our inertial odometry algorithm is superior to the
state-of-the-art filter-based and optimization-based visual-inertial odometry
as well as the state-of-the-art learned-inertial odometry in estimating the
pose of an autonomous racing drone. Additionally, we show that our system is
comparable to a visual-inertial odometry solution that uses a camera and
exploits the known gate location and appearance. We believe that the
application in autonomous drone racing paves the way for novel research in
inertial odometry for agile quadrotor flight.
