UniMSF: A Unified Multi-Sensor Fusion Framework for Intelligent Transportation System Global Localization

Intelligent transportation systems (ITS) localization is of significant importance as it provides fundamental position and orientation for autonomous operations like intelligent vehicles. Integrating diverse and complementary sensors such as global navigation satellite system (GNSS) and 4D-radar can...

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Bibliographic Details
Published inarXiv.org
Main Authors Liu, Wei, Zhu, Jiaqi, Zhuo, Guirong, Fu, Wufei, Meng, Zonglin, Lu, Yishi, Hua, Min, Qiao, Feng, Li, You, He, Yi, Lu, Xiong
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.09.2024
Subjects
Autonomous navigation
Data analysis
Effectiveness
Global navigation satellite system
Heterogeneity
Intelligent transportation systems
Intelligent vehicles
Localization
Modularity
Multisensor fusion
Noise measurement
Optimization
Outliers (statistics)
Position measurement
Position sensing
Satellite observation
Sensors
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Summary:Intelligent transportation systems (ITS) localization is of significant importance as it provides fundamental position and orientation for autonomous operations like intelligent vehicles. Integrating diverse and complementary sensors such as global navigation satellite system (GNSS) and 4D-radar can provide scalable and reliable global localization. Nevertheless, multi-sensor fusion encounters challenges including heterogeneity and time-varying uncertainty in measurements. Consequently, developing a reliable and unified multi-sensor framework remains challenging. In this paper, we introduce UniMSF, a comprehensive multi-sensor fusion localization framework for ITS, utilizing factor graphs. By integrating a multi-sensor fusion front-end, alongside outlier detection\&noise model estimation, and a factor graph optimization back-end, this framework accomplishes efficient fusion and ensures accurate localization for ITS. Specifically, in the multi-sensor fusion front-end module, we tackle the measurement heterogeneity among different modality sensors and establish effective measurement models. Reliable outlier detection and data-driven online noise estimation methods ensure that back-end optimization is immune to interference from outlier measurements. In addition, integrating multi-sensor observations via factor graph optimization offers the advantage of \enquote{plug and play}. Notably, our framework features high modularity and is seamlessly adapted to various sensor configurations. We demonstrate the effectiveness of the proposed framework through real vehicle tests by tightly integrating GNSS pseudorange and carrier phase information with IMU, and 4D-radar.
ISSN:2331-8422