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Visual place recognition is challenging in the urban environment and isusually viewed as a large scale image retrieval task. The intrinsic challengesin place recognition exist that the confusing objects such as cars and treesfrequently occur in the complex urban scene, and buildings with repetitivestructures may cause over-counting and the burstiness problem degrading theimage representations. To address these problems, we present an Attention-basedPyramid Aggregation Network (APANet), which is trained in an end-to-end mannerfor place recognition. One main component of APANet, the spatial pyramidpooling, can effectively encode the multi-size buildings containinggeo-information. The other one, the attention block, is adopted as a regionevaluator for suppressing the confusing regional features while highlightingthe discriminative ones. When testing, we further propose a simple yeteffective PCA power whitening strategy, which significantly improves the widelyused PCA whitening by reasonably limiting the impact of over-counting.Experimental evaluations demonstrate that the proposed APANet outperforms thestate-of-the-art methods on two place recognition benchmarks, and generalizeswell on standard image retrieval datasets.

Attention-based Pyramid Aggregation Network for Visual Place Recognition