  Two crucial performance criteria for the deployment of visual localization
are speed and accuracy. Current research on visual localization with neural
networks is limited to examining methods for enhancing the accuracy of networks
across various datasets. How to expedite the re-localization process within
deep neural network architectures still needs further investigation. In this
paper, we present a novel approach for accelerating visual re-localization in
practice. A tree-like search strategy, built on the keyframes extracted by a
visual clustering algorithm, is designed for matching acceleration. Our method
has been validated on two tasks across three public datasets, allowing for 50
up to 90 percent time saving over the baseline while not reducing location
accuracy.
