Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV

TL;DR

This paper introduces a computationally efficient spectral-normalized memory neural network for UAV position estimation in GPS-denied environments, integrating sensor data with Kalman filtering for improved accuracy.

Contribution

A novel spectral-normalized memory neural network (SN-MNN) for UAV position prediction that is lightweight, stable, and does not require additional sensors.

Findings

Achieves the lowest RMSE compared to state-of-the-art algorithms.

Operates without extra onboard sensors, reducing complexity.

Demonstrated effective in real flight data from a micro-UAV.

Abstract

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.