  Visual Place Recognition is an essential component of systems for camera
localization and loop closure detection, and it has attracted widespread
interest in multiple domains such as computer vision, robotics and AR/VR. In
this work, we propose a faster, lighter and stronger approach that can generate
models with fewer parameters and can spend less time in the inference stage. We
designed RepVGG-lite as the backbone network in our architecture, it is more
discriminative than other general networks in the Place Recognition task.
RepVGG-lite has more speed advantages while achieving higher performance. We
extract only one scale patch-level descriptors from global descriptors in the
feature extraction stage. Then we design a trainable feature matcher to exploit
both spatial relationships of the features and their visual appearance, which
is based on the attention mechanism. Comprehensive experiments on challenging
benchmark datasets demonstrate the proposed method outperforming recent other
state-of-the-art learned approaches, and achieving even higher inference speed.
Our system has 14 times less params than Patch-NetVLAD, 6.8 times lower
theoretical FLOPs, and run faster 21 and 33 times in feature extraction and
feature matching. Moreover, the performance of our approach is 0.5\% better
than Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street Level
Sequences dataset to conduct experiments for all other challenging conditions.
