论文标题
在线fgo：在线连续时间因素图优化，以时为中心的多传感器融合，以在大规模环境中进行稳健本地化
onlineFGO: Online Continuous-Time Factor Graph Optimization with Time-Centric Multi-Sensor Fusion for Robust Localization in Large-Scale Environments
论文作者
论文摘要
由于环境大规模且复杂的环境，在城市地区的准确且一致的车辆定位具有挑战性。在本文中，我们提出了一种基于时间为中心的基于图形优化的本地化方法，该方法将多个传感器测量与车辆定位任务的连续时间轨迹表示融合在一起。我们通过按时间确定的状态来概括与任何空间传感器测量无关的图形结构。由于连续时间中的轨迹表示可以在任意时间启用查询状态，因此可以将传感器测量值分解在图表上，而无需状态比对。我们集成了不同的GNS观察结果：伪曲线，三角形和时间差异的载流子相（TDCP），以确保全局参考并融合激光射频测量法的相对运动以提高本地化一致性，而GNSS观察结果不可用。关于一般绩效，不同因素的影响和高参数设置的实验是在亚当市的一项现实世界测量活动中进行的，其中包含不同的城市场景。我们的结果表明，在城市场景中，平均2D误差为0.99m，状态估计一致。
Accurate and consistent vehicle localization in urban areas is challenging due to the large-scale and complicated environments. In this paper, we propose onlineFGO, a novel time-centric graph-optimization-based localization method that fuses multiple sensor measurements with the continuous-time trajectory representation for vehicle localization tasks. We generalize the graph construction independent of any spatial sensor measurements by creating the states deterministically on time. As the trajectory representation in continuous-time enables querying states at arbitrary times, incoming sensor measurements can be factorized on the graph without requiring state alignment. We integrate different GNSS observations: pseudorange, deltarange, and time-differenced carrier phase (TDCP) to ensure global reference and fuse the relative motion from a LiDAR-odometry to improve the localization consistency while GNSS observations are not available. Experiments on general performance, effects of different factors, and hyper-parameter settings are conducted in a real-world measurement campaign in Aachen city that contains different urban scenarios. Our results show an average 2D error of 0.99m and consistent state estimation in urban scenarios.
