  Visual place recognition (VPR) is a fundamental task for many applications
such as robot localization and augmented reality. Recently, the hierarchical
VPR methods have received considerable attention due to the trade-off between
accuracy and efficiency. They usually first use global features to retrieve the
candidate images, then verify the spatial consistency of matched local features
for re-ranking. However, the latter typically relies on the RANSAC algorithm
for fitting homography, which is time-consuming and non-differentiable. This
makes existing methods compromise to train the network only in global feature
extraction. Here, we propose a transformer-based deep homography estimation
(DHE) network that takes the dense feature map extracted by a backbone network
as input and fits homography for fast and learnable geometric verification.
Moreover, we design a re-projection error of inliers loss to train the DHE
network without additional homography labels, which can also be jointly trained
with the backbone network to help it extract the features that are more
suitable for local matching. Extensive experiments on benchmark datasets show
that our method can outperform several state-of-the-art methods. And it is more
than one order of magnitude faster than the mainstream hierarchical VPR methods
using RANSAC. The code is released at https://github.com/Lu-Feng/DHE-VPR.
