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Visual place recognition is a challenging task for applications such asautonomous driving navigation and mobile robot localization. Distractingelements presenting in complex scenes often lead to deviations in theperception of visual place. To address this problem, it is crucial to integrateinformation from only task-relevant regions into image representations. In thispaper, we introduce a novel holistic place recognition model, TransVPR, basedon vision Transformers. It benefits from the desirable property of theself-attention operation in Transformers which can naturally aggregatetask-relevant features. Attentions from multiple levels of the Transformer,which focus on different regions of interest, are further combined to generatea global image representation. In addition, the output tokens from Transformerlayers filtered by the fused attention mask are considered as key-patchdescriptors, which are used to perform spatial matching to re-rank thecandidates retrieved by the global image features. The whole model allowsend-to-end training with a single objective and image-level supervision.TransVPR achieves state-of-the-art performance on several real-world benchmarkswhile maintaining low computational time and storage requirements.

TransVPR: Transformer-based place recognition with multi-level attention aggregation