Spiking Neural-Invariant Kalman Fusion for Accurate Localization Using Low-Cost IMUs

2026-01-13

AI Summary

This work addresses the significant degradation in localization accuracy caused by complex, nonlinear, time-varying noise in low-precision inertial measurement units (IMUs). To tackle this challenge, the paper presents the first integration of spiking neural networks (SNNs) with the invariant extended Kalman filter (InEKF). The proposed approach leverages SNNs to extract motion features from noisy IMU sequences and dynamically adapts the InEKF’s noise covariance matrices, enabling adaptive modeling and compensation of IMU noise. Evaluated on the KITTI benchmark and real-world robotic datasets, the method substantially outperforms existing state-of-the-art techniques, achieving higher pose estimation accuracy while demonstrating strong robustness against sensor noise.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Low-cost inertial measurement units (IMUs) are widely utilized in mobile robot localization due to their affordability and ease of integration. However, their complex, nonlinear, and time-varying noise characteristics often lead to significant degradation in localization accuracy when applied directly for dead reckoning. To overcome this limitation, we propose a novel brain-inspired state estimation framework that combines a spiking neural network (SNN) with an invariant extended Kalman filter (InEKF). The SNN is designed to extract motion-related features from long sequences of IMU data affected by substantial random noise and is trained via a surrogate gradient descent algorithm to enable dynamic adaptation of the covariance noise parameter within the InEKF. By fusing the SNN output with raw IMU measurements, the proposed method enhances the robustness and accuracy of pose estimation. Extensive experiments conducted on the KITTI dataset and real-world data collected using a mobile robot equipped with a low-cost IMU demonstrate that the proposed approach outperforms state-of-the-art methods in localization accuracy and exhibits strong robustness to sensor noise, highlighting its potential for real-world mobile robot applications.

Problem

• low-cost IMUs
• localization accuracy
• nonlinear noise
• time-varying noise
• dead reckoning

Innovation

• Spiking Neural Network
• Invariant Extended Kalman Filter
• Low-cost IMU
• Noise-adaptive Covariance
• Mobile Robot Localization