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 0.48m while fusing raw GNSS observations with lidar odometry in tight coupling.

翻译：在城市高密度区域实现精确且鲁棒的车辆定位极具挑战性。在复杂且大规模的此类环境中，传感器常受到干扰。本文提出GNSS-FGO——一种在线全局轨迹估计器，通过融合GNSS观测值与多种传感器测量数据实现鲁棒车辆定位。在GNSS-FGO中，我们采用高斯过程回归的连续时间轨迹表示，将异步传感器测量值融入因子图。这使得系统可在任意时间戳查询状态，从而在无需严格状态与测量同步的情况下融合传感器观测值。因此，所提方法构建了一种通用的多传感器融合因子图。为评估并研究不同GNSS融合策略，我们分别以松耦合和紧耦合方式将GNSS测量值与速度传感器、IMU及激光雷达里程计进行融合。实验研究采用来自亚琛、杜塞尔多夫和科隆的实测数据集，并对传感器观测值、平滑器类型及超参数调优展开了全面讨论。结果表明，在经典多传感器融合方法因传感器退化而失效的密集城区，所提方法能够实现鲁棒的轨迹估计。在包含17公里亚琛路线的测试序列中，采用紧耦合方式融合原始GNSS观测值与激光雷达里程计时，该方法实现了0.48米的平均二维定位误差。