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Accurate and consistent vehicle localization in urban areas is challengingdue to the large-scale and complicated environments. In this paper, we proposeonlineFGO, a novel time-centric graph-optimization-based localization methodthat fuses multiple sensor measurements with the continuous-time trajectoryrepresentation for vehicle localization tasks. We generalize the graphconstruction independent of any spatial sensor measurements by creating thestates deterministically on time. As the trajectory representation incontinuous-time enables querying states at arbitrary times, incoming sensormeasurements can be factorized on the graph without requiring state alignment.We integrate different GNSS observations: pseudorange, deltarange, andtime-differenced carrier phase (TDCP) to ensure global reference and fuse therelative motion from a LiDAR-odometry to improve the localization consistencywhile GNSS observations are not available. Experiments on general performance,effects of different factors, and hyper-parameter settings are conducted in areal-world measurement campaign in Aachen city that contains different urbanscenarios. Our results show an average 2D error of 0.99m and consistent stateestimation in urban scenarios.

onlineFGO: Online Continuous-Time Factor Graph Optimization with  Time-Centric Multi-Sensor Fusion for Robust Localization in Large-Scale  Environments