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Low-overhead visual place recognition (VPR) is a highly active researchtopic. Mobile robotics applications often operate under low-end hardware, andeven more hardware capable systems can still benefit from freeing up onboardsystem resources for other navigation tasks. This work addresses lightweightVPR by proposing a novel system based on the combination of binary-weightedclassifier networks with a one-dimensional convolutional network, dubbedmerger. Recent work in fusing multiple VPR techniques has mainly focused onincreasing VPR performance, with computational efficiency not being highlyprioritized. In contrast, we design our technique prioritizing low inferencetimes, taking inspiration from the machine learning literature where theefficient combination of classifiers is a heavily researched topic. Ourexperiments show that the merger achieves inference times as low as 1millisecond, being significantly faster than other well-established lightweightVPR techniques, while achieving comparable or superior VPR performance onseveral visual changes such as seasonal variations and viewpoint lateralshifts.

Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments