  Visual localization techniques often comprise a hierarchical localization
pipeline, with a visual place recognition module used as a coarse localizer to
initialize a pose refinement stage. While improving the pose refinement step
has been the focus of much recent research, most work on the coarse
localization stage has focused on improvements like increased invariance to
appearance change, without improving what can be loose error tolerances. In
this letter, we propose two methods which adapt image retrieval techniques used
for visual place recognition to the Bayesian state estimation formulation for
localization. We demonstrate significant improvements to the localization
accuracy of the coarse localization stage using our methods, whilst retaining
state-of-the-art performance under severe appearance change. Using extensive
experimentation on the Oxford RobotCar dataset, results show that our approach
outperforms comparable state-of-the-art methods in terms of precision-recall
performance for localizing image sequences. In addition, our proposed methods
provides the flexibility to contextually scale localization latency in order to
achieve these improvements. The improved initial localization estimate opens up
the possibility of both improved overall localization performance and modified
pose refinement techniques that leverage this improved spatial prior.
