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

TL;DR

This work tackles navigation degradation during GPS outages by introducing a brain-inspired GPS/INS fusion network (BGFN) that combines a spiking encoder with a spiking Transformer to predict GPS position increments from IMU history. The BGFN is embedded in a hybrid Kalman-filter framework, enabling online training when GPS is available and outage-time prediction to sustain navigation via pseudo-GPS increments. Experimental validation on public NaveGo data and real-field tests shows that BGFN substantially reduces inertial drift compared with traditional DL models, with notable improvements under prolonged outages and potential energy efficiency gains on neuromorphic hardware (~66.3% compared to standard Transformers). The method demonstrates the practical viability of neuromorphic-inspired fusion for robust, low-power navigation in GPS-denied environments and points to future work on hardware deployment and end-to-end spiking GPS/INS learning.

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.

Figures (10)

• Figure 1: A block diagram of a loosely coupled GPS/INS integration.
• Figure 2: The structure illustration of the LIF neuron.
• Figure 3: The GPS/INS integrated navigation system based on BGFN.
• Figure 4: The architecture of the BGFN.
• Figure 5: The Navego dataset trajectory.