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

Jul 16, 2026

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Although multi-source fusion positioning systems improve overall localization, accurate and reliable heading estimation remains difficult because heading has weak observability and lacks gravitational constraints in complex environments. Many existing solutions focus on one-time startup alignment, producing heading references that cannot adapt during long navigation or right after GNSS outages, leading to drift accumulation and sensitivity to observation noise. WinTA-GIL proposes temporal-window optimization integrating GNSS, IMU, and LiDAR, using LIO trajectories to register against filtered GNSS and performing adaptive re-estimation to trigger corrections when needed. Extensive experiments show improved accuracy and robustness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Introduction

Problem and motivation

Limitations of existing heading alignment methods

WinTA-GIL: Windowed Trajectory Alignment for GNSS-IMU-LiDAR Heading Refinement in Intermittent Signal Environments Kaixin Feng 1 , Zhichao Wen 1 , Zhaohong Liao2 , Xin Xia3 , You Li 1 arXiv :2607 .04879v 1 [ cs .RO] 6 Jul 2026 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 windowbased 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 stateof-the-art approaches in both estimation accuracy and system robustness. I. INTRODUCTION Accurate and reliable pose estimation serves as the foundation for autonomous robots to achieve robust environmental perception and intelligent decision-making. In complex and dynamic scenarios, multi-source fusion systems have emerged as the predominant paradigm, leveraging the complementary strengths of Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and Light Detection and Ranging (LiDAR) to maintain both global consistency and local precision. Although accelerometers *This work was partly supported by the National Natural Science Foundation of China (42274052) and the Wuhan Natural Science Foundation Project (2025041001010363) . 1 Kaixin Feng, Zhichao Wen, and You Li are with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430072, China. You Li is the corresponding [author.](author. kaixinfeng@whu.edu.cn)[ kaixinfeng@whu.edu.cn](author. kaixinfeng@whu.edu.cn), [zhichaowen@whu.edu.cn](zhichaowen@whu.edu.cn), [liyou@whu.edu.cn](liyou@whu.edu.cn) 2Zhaohong Liao is with the School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China. [liaozhaohong@whu.edu.cn](liaozhaohong@whu.edu.cn) 3Xin Xia is with the College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA. [xinxia@umich.edu](xinxia@umich.edu) Fig. 1: Impact of inaccurate heading estimation on trajectory consistency. The panels from left to right illustrate cumulative drift originating from initial errors, error propagation during dead-reckoning, and position discontinuity at the point of GNSS recovery. can effectively constrain pitch and roll angles by sensing the gravity vector, heading estimation remains a persistent challenge. Due to the lack of gravitational constraints and the weak observability inherent in stationary or uniform motion, the heading angle is highly susceptible to sensor biases and env

FAQs

Why is heading estimation difficult in GNSS-IMU-LiDAR fusion systems?

Heading estimation is challenging because heading lacks gravitational constraints and has weak observability in complex or stationary/uniform motion conditions. Sensor biases and environmental disturbances can strongly affect the heading angle.

What limitation do many existing methods have regarding heading refinement?

Most methods perform a single alignment during the startup phase and do not dynamically refine heading afterward. This makes the system vulnerable to accumulated inertial drift and observation noise during prolonged navigation or after GNSS recovery.

How does WinTA-GIL refine heading in intermittent GNSS signal environments?

WinTA-GIL uses a temporal-window optimization framework that integrates GNSS, IMU, and LiDAR. It leverages high-precision local trajectories from LiDAR-Inertial Odometry (LIO) to align with filtered GNSS observations and applies an adaptive re-estimation mechanism to trigger heading corrections when necessary.

Jul 16, 2026

Upload by Violet