用因子图优化实现实时松耦合GNSS与IMU融合，提升城市峡谷环境下的定位可用性。
Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization
- 基于因子图优化构建松耦合架构，实现实时融合
- 在UrbanNav-HK-MediumUrban-1数据集上实现实时运行，服务可用性提升
- 平衡精度、可用性与计算效率，适合高动态场景应用
精准定位、导航与授时（PNT）是现代技术运行的基础，也是自动驾驶系统的关键支撑。全球导航卫星系统（GNSS）保障室外定位，而将GNSS与惯性测量单元（IMU）融合可显著提升定位性能。本文提出一种基于因子图优化（FGO）的松耦合架构，用于实时融合GNSS与IMU数据。针对传统FGO计算开销大、多用于离线处理的问题，本文重点评估其在复杂环境（如城市峡谷）中实时运行的定位精度与服务可用性。在UrbanNav-HK-MediumUrban-1数据集上的实验表明，该方法可实现实时运行，相比批处理式FGO，服务可用性更高。尽管精度略有下降，但论文深入分析了精度、可用性与计算效率之间的权衡关系，为实时FGO驱动的GNSS/IMU融合提供了实用参考。
原文摘要 · Abstract (English)
Accurate positioning, navigation, and timing (PNT) is fundamental to the operation of modern technologies and a key enabler of autonomous systems. A very important component of PNT is the Global Navigation Satellite System (GNSS) which ensures outdoor positioning. Modern research directions have pushed the performance of GNSS localization to new heights by fusing GNSS measurements with other sensory information, mainly measurements from Inertial Measurement Units (IMU). In this paper, we propose a loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework. Because the FGO method can be computationally challenging and often used as a post-processing method, our focus is on assessing its localization accuracy and service availability while operating in real-time in challenging environments (urban canyons). Experimental results on the UrbanNav-HK-MediumUrban-1 dataset show that the proposed approach achieves real-time operation and increased service availability compared to batch FGO methods. While this improvement comes at the cost of reduced positioning accuracy, the paper provides a detailed analysis of the trade-offs between accuracy, availability, and computational efficiency that characterize real-time FGO-based GNSS/IMU fusion.
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