WinTA-GIL: Windowed Trajectory Alignment for GNSS-IMU-LiDAR Heading Refinement in Intermittent Signal Environments

2026-07-06

AI Summary

This work addresses the challenge of maintaining accurate and robust heading estimation in multi-sensor fusion systems under GNSS-denied or complex environments, where insufficient observability of heading and the absence of gravity constraints often lead to drift, and existing methods typically only correct heading during initialization without dynamic refinement. To overcome this limitation, we propose the WinTA-GIL framework, which formulates heading estimation as a trajectory consistency optimization problem between LiDAR-inertial odometry over a sliding time window and filtered GNSS observations. The approach further incorporates a state-aware adaptive re-estimation mechanism to enable repeatable, full-time heading refinement. Experimental results on both public and self-collected datasets demonstrate that our method significantly outperforms state-of-the-art approaches, achieving notable improvements in heading accuracy and overall system robustness.

Abstract

Although multi-source fusion positioning systems have achieved significant progress, accurate and reliable heading estimation remains a critical challenge due to the lack of gravitational constraints and the inherent weak observability of heading in complex environments. Most existing methodologies are specifically tailored for the startup phase, relying on a singular initial alignment to establish the heading reference. Consequently, these approaches lack the adaptability required to refine heading estimates dynamically, which renders the system highly vulnerable to accumulated drift and observation noise during prolonged navigation or immediately following GNSS signal outages. To address these limitations, this paper proposes WinTA-GIL, a novel heading refinement framework that integrates information from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and Light Detection and Ranging (LiDAR) through a temporal window-based optimization strategy. Unlike conventional alignment methods restricted to the startup phase, WinTA-GIL leverages high-precision local trajectories from LiDAR-Inertial Odometry (LIO) to register against filtered GNSS observations. This approach transforms heading estimation into a repeatable, trajectory-based consistency optimization problem. In particular, an adaptive re-estimation mechanism based on state discrimination is incorporated to trigger heading corrections whenever necessary, thereby effectively suppressing the inertial drift accumulated during challenging conditions. Extensive experiments on both open-source and self-collected datasets demonstrate that WinTA-GIL significantly outperforms state-of-the-art approaches in both estimation accuracy and system robustness.

Problem

• heading estimation
• GNSS-IMU-LiDAR fusion
• intermittent GNSS signal
• inertial drift
• weak observability

Innovation

• Windowed Trajectory Alignment
• Heading Refinement
• GNSS-IMU-LiDAR Fusion
• Adaptive Re-estimation
• Trajectory Consistency Optimization