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Visual Place Recognition is an essential component of systems for cameralocalization and loop closure detection, and it has attracted widespreadinterest in multiple domains such as computer vision, robotics and AR/VR. Inthis work, we propose a faster, lighter and stronger approach that can generatemodels with fewer parameters and can spend less time in the inference stage. Wedesigned RepVGG-lite as the backbone network in our architecture, it is morediscriminative than other general networks in the Place Recognition task.RepVGG-lite has more speed advantages while achieving higher performance. Weextract only one scale patch-level descriptors from global descriptors in thefeature extraction stage. Then we design a trainable feature matcher to exploitboth spatial relationships of the features and their visual appearance, whichis based on the attention mechanism. Comprehensive experiments on challengingbenchmark datasets demonstrate the proposed method outperforming recent otherstate-of-the-art learned approaches, and achieving even higher inference speed.Our system has 14 times less params than Patch-NetVLAD, 6.8 times lowertheoretical FLOPs, and run faster 21 and 33 times in feature extraction andfeature matching. Moreover, the performance of our approach is 0.5\% betterthan Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street LevelSequences dataset to conduct experiments for all other challenging conditions.

A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition