  6-DoF visual localization systems utilize principled approaches rooted in 3D
geometry to perform accurate camera pose estimation of images to a map. Current
techniques use hierarchical pipelines and learned 2D feature extractors to
improve scalability and increase performance. However, despite gains in typical
<EMAIL_ADDRESS>type metrics, these systems still have limited utility for
real-world applications like autonomous vehicles because of their `worst' areas
of performance - the locations where they provide insufficient recall at a
certain required error tolerance. Here we investigate the utility of using
`place specific configurations', where a map is segmented into a number of
places, each with its own configuration for modulating the pose estimation
step, in this case selecting a camera within a multi-camera system. On the Ford
AV benchmark dataset, we demonstrate substantially improved worst-case
localization performance compared to using off-the-shelf pipelines - minimizing
the percentage of the dataset which has low recall at a certain error
tolerance, as well as improved overall localization performance. Our proposed
approach is particularly applicable to the crowdsharing model of autonomous
vehicle deployment, where a fleet of AVs are regularly traversing a known
route.
