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

2026-01-13

AI Summary

This work addresses the rapid degradation of positioning accuracy in low-cost inertial navigation systems during GPS outages, which is primarily caused by sensor noise and bias. To mitigate this issue, the authors propose a brain-inspired GPS/INS fusion network (BGFN), which uniquely integrates spiking neural networks with a Transformer architecture. By jointly modeling the spatiotemporal dynamics of IMU signals through a spiking encoder and a spiking Transformer, BGFN enables robust estimation of vehicle motion. Inspired by the biological brain’s spatiotemporal perception mechanisms, the method demonstrates superior performance on both real-world scenarios and public datasets, significantly improving positioning accuracy and navigation continuity during prolonged GPS interruptions compared to conventional deep learning approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• GPS outages
• inertial navigation
• sensor bias
• navigation accuracy
• positioning continuity

Innovation

• spiking neural networks
• GPS/INS fusion
• spiking Transformer
• inertial navigation
• brain-inspired computing