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This paper presents a deep learning approach to aid dead-reckoning (DR)navigation using a limited sensor suite. A Recurrent Neural Network (RNN) wasdeveloped to predict the relative horizontal velocities of an AutonomousUnderwater Vehicle (AUV) using data from an IMU, pressure sensor, and controlinputs. The RNN network is trained using experimental data, where a dopplervelocity logger (DVL) provided ground truth velocities. The predictions of therelative velocities were implemented in a dead-reckoning algorithm toapproximate north and east positions. The studies in this paper were twofold I)Experimental data from a Long-Range AUV was investigated. Datasets from aseries of surveys in Monterey Bay, California (U.S) were used to train and testthe RNN network. II) The second study explore datasets generated by a simulatedautonomous underwater glider. Environmental variables e.g ocean currents wereimplemented in the simulation to reflect real ocean conditions. The proposedneural network approach to DR navigation was compared to the on-boardnavigation system and ground truth simulated positions.

A Deep Learning Approach To Dead-Reckoning Navigation For Autonomous Underwater Vehicles With Limited Sensor Payloads