  In this work, we propose an interoceptive-only state estimation system for a
quadrotor with deep neural network processing, where the quadrotor dynamics is
considered as a perceptive supplement of the inertial kinematics. To improve
the precision of multi-sensor fusion, we train cascaded networks on real-world
quadrotor flight data to learn IMU kinematic properties, quadrotor dynamic
characteristics, and motion states of the quadrotor along with their
uncertainty information, respectively. This encoded information empowers us to
address the issues of IMU bias stability, quadrotor dynamics, and multi-sensor
calibration during sensor fusion. The above multi-source information is fused
into a two-stage Extended Kalman Filter (EKF) framework for better estimation.
Experiments have demonstrated the advantages of our proposed work over several
conventional and learning-based methods.
