  Visual-inertial odometry (VIO) is the most common approach for estimating the
state of autonomous micro aerial vehicles using only onboard sensors. Existing
methods improve VIO performance by including a dynamics model in the estimation
pipeline. However, such methods degrade in the presence of low-fidelity vehicle
models and continuous external disturbances, such as wind. Our proposed method,
HDVIO, overcomes these limitations by using a hybrid dynamics model that
combines a point-mass vehicle model with a learning-based component that
captures complex aerodynamic effects. HDVIO estimates the external force and
the full robot state by leveraging the discrepancy between the actual motion
and the predicted motion of the hybrid dynamics model. Our hybrid dynamics
model uses a history of thrust and IMU measurements to predict the vehicle
dynamics. To demonstrate the performance of our method, we present results on
both public and novel drone dynamics datasets and show real-world experiments
of a quadrotor flying in strong winds up to 25 km/h. The results show that our
approach improves the motion and external force estimation compared to the
state-of-the-art by up to 33% and 40%, respectively. Furthermore, differently
from existing methods, we show that it is possible to predict the vehicle
dynamics accurately while having no explicit knowledge of its full state.
