Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps so that sensor observations are fused without requiring strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Duesseldorf, and Cologne in experimental studies and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas, where the classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17km route through Aachen, the proposed method results in a mean 2D positioning error of 0.19m for loosely coupled GNSS fusion and 0.48m while fusing raw GNSS observations with lidar odometry in tight coupling.

翻译：在城市密集区域实现高精度鲁棒的车载定位具有挑战性，传感器在这些复杂大规模环境中经常受到干扰。本文提出GNSS-FGO——一种在线全局轨迹估计器，通过融合GNSS观测值与多传感器测量实现鲁棒的车载定位。在GNSS-FGO中，我们采用高斯过程回归的连续时间轨迹表示，将异步传感器测量融入因子图。该方法允许在任意时间戳查询状态，从而无需严格的状态-测量同步即可融合传感器观测数据。因此，所提方法构建了适用于多传感器融合的广义因子图。为评估不同GNSS融合策略，我们分别采用松耦合和紧耦合方式融合GNSS测量与速度传感器、IMU及激光雷达里程计数据。实验研究使用了在亚琛、杜塞尔多夫和科隆的实测数据集，并对传感器观测、平滑器类型及超参数调优进行了全面讨论。结果表明，所提方法能在经典多传感器融合因传感器退化而失效的城市密集区域实现鲁棒轨迹估计。在包含17公里亚琛路段的测试序列中，松耦合GNSS融合的平均二维定位误差为0.19米，紧耦合融合原始GNSS观测与激光雷达里程计时误差为0.48米。