  Vision-based localization for autonomous driving has been of great interest
among researchers. When a pre-built 3D map is not available, the techniques of
visual simultaneous localization and mapping (SLAM) are typically adopted. Due
to error accumulation, visual SLAM (vSLAM) usually suffers from long-term
drift. This paper proposes a framework to increase the localization accuracy by
fusing the vSLAM with a deep-learning-based ground-to-satellite (G2S) image
registration method. In this framework, a coarse (spatial correlation bound
check) to fine (visual odometry consistency check) method is designed to select
the valid G2S prediction. The selected prediction is then fused with the SLAM
measurement by solving a scaled pose graph problem. To further increase the
localization accuracy, we provide an iterative trajectory fusion pipeline. The
proposed framework is evaluated on two well-known autonomous driving datasets,
and the results demonstrate the accuracy and robustness in terms of vehicle
localization.
