A brain-inspired information fusion method for enhancing robot GPS outages navigation

TL;DR

This paper introduces a brain-inspired neural network that fuses GPS and inertial data to improve robot navigation during GPS outages, leveraging spiking neural networks for better accuracy and reliability.

Contribution

The paper presents a novel spiking neural network architecture combining a spiking Transformer and encoder for GPS/INS fusion, inspired by brain mechanisms.

Findings

Higher accuracy in navigation during GPS outages

Enhanced reliability over traditional deep learning methods

Effective real-world and dataset validation

Abstract

Low-cost inertial navigation systems (INS) are prone to sensor biases and measurement noise, which lead to rapid degradation of navigation accuracy during global positioning system (GPS) outages. To address this challenge and improve positioning continuity in GPS-denied environments, this paper proposes a brain-inspired GPS/INS fusion network (BGFN) based on spiking neural networks (SNNs). The BGFN architecture integrates a spiking Transformer with a spiking encoder to simultaneously extract spatial features from inertial measurement unit (IMU) signals and capture their temporal dynamics. By modeling the relationship between vehicle attitude, specific force, angular rate, and GPS-derived position increments, the network leverages both current and historical IMU data to estimate vehicle motion. The effectiveness of the proposed method is evaluated through real-world field tests and experiments on public datasets. Compared to conventional deep learning approaches, the results demonstrate that BGFN achieves higher accuracy and enhanced reliability in navigation performance, particularly under prolonged GPS outages.