  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.
