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arXiv 2409.12426cs.RO
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UniMSF：面向智能交通系统全局定位的统一多传感器融合框架

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

School of Automotive Studies, Tongji University（同济大学汽车学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Shanghai Gongji Technology Co., Ltd（上海同济科技有限公司）；the University of Birmingham（伯明翰大学）；Institute for Automotive Engineering (ika), RWTH Aachen University（亚琛工业大学汽车工程研究所）；Wuhan University（武汉大学）；Wuhan University of Technology（武汉理工大学）

Wei Liu, Jiaqi Zhu, Guirong Zhuo, Wufei Fu, Zonglin Meng, Yishi Lu, Min Hua, Feng Qiao, You Li, Yi He, Lu Xiong

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AI总结：

提出UniMSF，一个基于因子图的统一多传感器融合定位框架，通过前端异质性处理、异常检测与噪声估计及后端优化，实现GNSS、IMU和4D雷达的紧密集成，提供可靠全局定位。

摘要：

智能交通系统（ITS）的定位具有重要意义，因为它为智能车辆等自主操作提供了基本的位置和姿态信息。集成全球导航卫星系统（GNSS）和4D雷达等多样且互补的传感器，可以提供可扩展且可靠的全局定位。然而，多传感器融合面临包括测量中的异质性和时变不确定性等挑战。因此，开发一个可靠且统一的多传感器框架仍然具有挑战性。在本文中，我们介绍了UniMSF，一个基于因子图的、面向ITS的全面多传感器融合定位框架。通过集成多传感器融合前端、异常检测与噪声模型估计以及因子图优化后端，该框架实现了高效融合，并确保ITS的准确定位。具体而言，在多传感器融合前端模块中，我们解决了不同模态传感器之间的测量异质性，并建立了有效的测量模型。可靠的异常检测和数据驱动的在线噪声估计方法确保后端优化不受异常测量干扰。此外，通过因子图优化集成多传感器观测，具有“即插即用”的优势。值得注意的是，我们的框架具有高模块化特性，并能无缝适应各种传感器配置。我们通过真实车辆测试，紧密集成GNSS伪距和载波相位信息与IMU及4D雷达，证明了所提框架的有效性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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