  This paper addresses the problem of position estimation in UAVs operating in
a cluttered environment where GPS information is unavailable. A model
learning-based approach is proposed that takes in the rotor RPMs and past state
as input and predicts the one-step-ahead position of the UAV using a novel
spectral-normalized memory neural network (SN-MNN). The spectral normalization
guarantees stable and reliable prediction performance. The predicted position
is transformed to global coordinate frame which is then fused along with the
odometry of other peripheral sensors like IMU, barometer, compass etc., using
the onboard extended Kalman filter to estimate the states of the UAV. The
experimental flight data collected from a motion capture facility using a
micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay
the flight data using the proposed algorithm, and the estimated position is
compared with actual ground truth data. The proposed algorithm doesn't require
any additional onboard sensors, and is computationally light. The performance
of the proposed approach is compared with the current state-of-art GPS-denied
algorithms, and it can be seen that the proposed algorithm has the least RMSE
for position estimates.
