Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV
Nishanth Rao, Suresh Sundaram, Varun Raghavendra

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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Inertial Sensor and Navigation · Target Tracking and Data Fusion in Sensor Networks
