Manifold Factor Graph Optimization-Based GNSS/IMU Integrated Navigation Testing Platform for Urban Vehicles

Ning Liu; Bin Hu; Junfang Fan; Zhong Su; Kai Shen; Huamin Jie; Wensong Wang; Zhenyu Zhao

doi:10.1109/TVT.2026.3674659

Manifold Factor Graph Optimization-Based GNSS/IMU Integrated Navigation Testing Platform for Urban Vehicles

Ning Liu
, Bin Hu
, Junfang Fan
, Zhong Su
, Kai Shen
, Huamin Jie
, Wensong Wang^*
, Zhenyu Zhao

^*此作品的通讯作者

Beijing Information Science & Technology University
Beijing Institute of Technology
Nanyang Technological University
National University of Singapore

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Multisource sensor fusion has become a research focus in navigation and positioning. However, the Kalman filter algorithm faces issues with real-time performance and positioning accuracy when handling asynchronous data fusion. The development of factor graph algorithms has significantly improved these issues. Meanwhile, research on vehicle-based manifold factor graph optimization algorithms in urban environments is relatively scarce. Therefore, this paper proposes a GNSS/IMU integrated navigation testing platform based on manifold factor graph optimization. The platform is designed to analyze the real-time performance and accuracy of the optimization algorithm under different path ranges. It optimizes IMU pre-integration using Lie group algorithms and constructs a manifold pre-integration factor model with keyframes. The testing algorithm integrates IMU pre-integration factors, GNSS factors, and state prior factors to achieve synchronous multisource fusion. By optimizing a sliding window, it performs nonlinear optimization of variables within the window and outputs the final navigation state. Simulation results show the proposed testing algorithm improves positioning accuracy by 96.67%, 95.36%, and 89.55% compared to three other algorithms. It maintains execution times below 10 seconds, demonstrating high real-time performance and efficiency.

源语言	英语
期刊	IEEE Transactions on Vehicular Technology
DOI	https://doi.org/10.1109/TVT.2026.3674659
出版状态	已接受/待刊 - 2026
已对外发布	是

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@article{f866c33fc0b64717ac3f1d8f6a413b7c,

title = "Manifold Factor Graph Optimization-Based GNSS/IMU Integrated Navigation Testing Platform for Urban Vehicles",

abstract = "Multisource sensor fusion has become a research focus in navigation and positioning. However, the Kalman filter algorithm faces issues with real-time performance and positioning accuracy when handling asynchronous data fusion. The development of factor graph algorithms has significantly improved these issues. Meanwhile, research on vehicle-based manifold factor graph optimization algorithms in urban environments is relatively scarce. Therefore, this paper proposes a GNSS/IMU integrated navigation testing platform based on manifold factor graph optimization. The platform is designed to analyze the real-time performance and accuracy of the optimization algorithm under different path ranges. It optimizes IMU pre-integration using Lie group algorithms and constructs a manifold pre-integration factor model with keyframes. The testing algorithm integrates IMU pre-integration factors, GNSS factors, and state prior factors to achieve synchronous multisource fusion. By optimizing a sliding window, it performs nonlinear optimization of variables within the window and outputs the final navigation state. Simulation results show the proposed testing algorithm improves positioning accuracy by 96.67\%, 95.36\%, and 89.55\% compared to three other algorithms. It maintains execution times below 10 seconds, demonstrating high real-time performance and efficiency.",

keywords = "Factor graph, inertial navigation system (INS), manifold pre-integration, nonlinear optimization",

author = "Ning Liu and Bin Hu and Junfang Fan and Zhong Su and Kai Shen and Huamin Jie and Wensong Wang and Zhenyu Zhao",

note = "Publisher Copyright: {\textcopyright} 1967-2012 IEEE.",

year = "2026",

doi = "10.1109/TVT.2026.3674659",

language = "English",

journal = "IEEE Transactions on Vehicular Technology",

issn = "0018-9545",

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T1 - Manifold Factor Graph Optimization-Based GNSS/IMU Integrated Navigation Testing Platform for Urban Vehicles

AU - Liu, Ning

AU - Hu, Bin

AU - Fan, Junfang

AU - Su, Zhong

AU - Shen, Kai

AU - Jie, Huamin

AU - Wang, Wensong

AU - Zhao, Zhenyu

PY - 2026

Y1 - 2026

N2 - Multisource sensor fusion has become a research focus in navigation and positioning. However, the Kalman filter algorithm faces issues with real-time performance and positioning accuracy when handling asynchronous data fusion. The development of factor graph algorithms has significantly improved these issues. Meanwhile, research on vehicle-based manifold factor graph optimization algorithms in urban environments is relatively scarce. Therefore, this paper proposes a GNSS/IMU integrated navigation testing platform based on manifold factor graph optimization. The platform is designed to analyze the real-time performance and accuracy of the optimization algorithm under different path ranges. It optimizes IMU pre-integration using Lie group algorithms and constructs a manifold pre-integration factor model with keyframes. The testing algorithm integrates IMU pre-integration factors, GNSS factors, and state prior factors to achieve synchronous multisource fusion. By optimizing a sliding window, it performs nonlinear optimization of variables within the window and outputs the final navigation state. Simulation results show the proposed testing algorithm improves positioning accuracy by 96.67%, 95.36%, and 89.55% compared to three other algorithms. It maintains execution times below 10 seconds, demonstrating high real-time performance and efficiency.

AB - Multisource sensor fusion has become a research focus in navigation and positioning. However, the Kalman filter algorithm faces issues with real-time performance and positioning accuracy when handling asynchronous data fusion. The development of factor graph algorithms has significantly improved these issues. Meanwhile, research on vehicle-based manifold factor graph optimization algorithms in urban environments is relatively scarce. Therefore, this paper proposes a GNSS/IMU integrated navigation testing platform based on manifold factor graph optimization. The platform is designed to analyze the real-time performance and accuracy of the optimization algorithm under different path ranges. It optimizes IMU pre-integration using Lie group algorithms and constructs a manifold pre-integration factor model with keyframes. The testing algorithm integrates IMU pre-integration factors, GNSS factors, and state prior factors to achieve synchronous multisource fusion. By optimizing a sliding window, it performs nonlinear optimization of variables within the window and outputs the final navigation state. Simulation results show the proposed testing algorithm improves positioning accuracy by 96.67%, 95.36%, and 89.55% compared to three other algorithms. It maintains execution times below 10 seconds, demonstrating high real-time performance and efficiency.

KW - Factor graph

KW - inertial navigation system (INS)

KW - manifold pre-integration

KW - nonlinear optimization

UR - https://www.scopus.com/pages/publications/105033267405

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DO - 10.1109/TVT.2026.3674659

M3 - Article

AN - SCOPUS:105033267405

SN - 0018-9545

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

ER -

Manifold Factor Graph Optimization-Based GNSS/IMU Integrated Navigation Testing Platform for Urban Vehicles

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