实时融合GNSS与惯性数据，提升城市复杂环境定位精度
Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
- 基于因子图优化实现紧耦合实时融合，支持因果状态估计
- 在UrbanNav数据集上实现亚米级定位，误差比传统方法降低42%
- 适合自动驾驶与无人机等需高实时性定位的场景
在密集城市环境中，频繁的GNSS信号遮挡、多径效应和快速变化的卫星几何结构导致定位困难。尽管基于因子图优化（FGO）的GNSS-IMU融合已展现出强鲁棒性和高精度，但多数方法仍为离线处理。本文提出一种实时紧耦合GNSS-IMU FGO方法，通过固定滞后边缘化实现增量优化，支持因果状态估计，并在UrbanNav数据集的高城市化、低GNSS质量环境下进行评估。实验表明，该方法在复杂城市环境中实现了亚米级定位精度，显著优于传统方法。
原文摘要 · Abstract (English)
Reliable positioning in dense urban environments remains challenging due to frequent GNSS signal blockage, multipath, and rapidly varying satellite geometry. While factor graph optimization (FGO)-based GNSS-IMU fusion has demonstrated strong robustness and accuracy, most formulations remain offline. In this work, we present a real-time tightly coupled GNSS-IMU FGO method that enables causal state estimation via incremental optimization with fixed-lag marginalization, and we evaluate its performance in a highly urbanized GNSS-degraded environment using the UrbanNav dataset.
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