Deep learning-aided Laser Doppler Velocimeter-Inertial Measurement Unit Fusion for Robust Vehicle Localization in Global Navigation Satellite Systems-denied environments

Abstract

Achieving reliable and precise vehicle positioning is paramount for modern autonomous systems, yet it remains a formidable challenge in Global Navigation Satellite Systems (GNSS)-denied environments, especially when relying on ubiquitous low-cost Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Units (IMUs). This paper introduces a solution that enhances MEMS IMU capabilities by integrating two symmetrically mounted dual-beam Laser Doppler Velocimeters (LDVs). Our core innovation lies in leveraging two specialized Long Short-Term Memory (LSTM) networks that robustly regress the vehicle’s yaw and lateral velocities by effectively fusing both LDV and IMU outputs. To further elevate system accuracy, we propose an LDV outlier handling strategy and a method for LSTM prediction reliability detection designed to mitigate the adverse effects of anomalous network outputs. The vehicle velocities from the LDVs, augmented by our LSTM-derived yaw and lateral velocities, are then fused with MEMS IMU data within a Lie group-based Kalman filter. Experimental validation through two rigorous test sets demonstrates that our method significantly reduces system positioning errors under prolonged GNSS-denied conditions, outperforming existing LDV-based methods. This work underscores the potential of combining precise LDV measurements with the predictive power of deep learning and a robust Lie group-based data fusion strategy for accurate and reliable autonomous vehicle localization.

Keywords

• Micro-Electro-Mechanical Systems Inertial Measurement Unit
• Dual-beam Laser Doppler Velocimeter
• Long short-term memory network
• Yaw velocity

Recommended citation: Z. Xiang, Q. Wang, X. Nie, and J. Zhou, "Deep learning-aided Laser Doppler Velocimeter-Inertial Measurement Unit Fusion for Robust Vehicle Localization in Global Navigation Satellite Systems-denied environments," Eng. Appl. Artif. Intell, vol. 165, p. 113425, 2026.