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