  Visual place recognition is a challenging task for applications such as
autonomous driving navigation and mobile robot localization. Distracting
elements presenting in complex scenes often lead to deviations in the
perception of visual place. To address this problem, it is crucial to integrate
information from only task-relevant regions into image representations. In this
paper, we introduce a novel holistic place recognition model, TransVPR, based
on vision Transformers. It benefits from the desirable property of the
self-attention operation in Transformers which can naturally aggregate
task-relevant features. Attentions from multiple levels of the Transformer,
which focus on different regions of interest, are further combined to generate
a global image representation. In addition, the output tokens from Transformer
layers filtered by the fused attention mask are considered as key-patch
descriptors, which are used to perform spatial matching to re-rank the
candidates retrieved by the global image features. The whole model allows
end-to-end training with a single objective and image-level supervision.
TransVPR achieves state-of-the-art performance on several real-world benchmarks
while maintaining low computational time and storage requirements.
