Marine Vehicles Localization Using Grid Cells for Path Integration

TL;DR

This paper proposes a novel underwater vehicle localization method inspired by mammalian grid cells, using only velocity and orientation data to improve position estimation without GPS, validated through simulations.

Contribution

Introduces a bio-inspired grid cell-based model for AUV localization that relies solely on velocity and heading, enhancing underwater navigation accuracy.

Findings

Simulation results demonstrate reliable position estimation.

Method outperforms traditional Kalman filter approaches.

Bio-inspired model is computationally feasible for real-time use.

Abstract

Autonomous Underwater Vehicles (AUVs) are platforms used for research and exploration of marine environments. However, these types of vehicles face many challenges that hinder their widespread use in the industry. One of the main limitations is obtaining accurate position estimation, due to the lack of GPS signal underwater. This estimation is usually done with Kalman filters. However, new developments in the neuroscience field have shed light on the mechanisms by which mammals are able to obtain a reliable estimation of their current position based on external and internal motion cues. A new type of neuron, called Grid cells, has been shown to be part of path integration system in the brain. In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles. The model of grid cells used requires only the linear velocities together with heading orientation and provides a reliable estimation of the vehicle's position. We provide simulation results for an AUV which show the feasibility of our proposed methodology.