Visual place recognition (VPR) enables autonomous systems to localize
themselves within an environment using image information. While VPR techniques
built upon a Convolutional Neural Network (CNN) backbone dominate
state-of-the-art VPR performance, their high computational requirements make
them unsuitable for platforms equipped with low-end hardware. Recently, a
lightweight VPR system based on multiple bio-inspired classifiers, dubbed
DrosoNets, has been proposed, achieving great computational efficiency at the
cost of reduced absolute place retrieval performance. In this work, we propose
a novel multi-DrosoNet localization system, dubbed RegionDrosoNet, with
significantly improved VPR performance, while preserving a low-computational
profile. Our approach relies on specializing distinct groups of DrosoNets on
differently sliced partitions of the original image, increasing extrinsic model
differentiation. Furthermore, we introduce a novel voting module to combine the
outputs of all DrosoNets into the final place prediction which considers
multiple top refence candidates from each DrosoNet. RegionDrosoNet outperforms
other lightweight VPR techniques when dealing with both appearance changes and
viewpoint variations. Moreover, it competes with computationally expensive
methods on some benchmark datasets at a small fraction of their online
inference time.

提出了一种改进的多 DrosoNet 定位系统，名为 RegionDrosoNet，具有显著提高的 VPR 性能和低计算复杂度，充分考虑了外部模型差异，并引入了新的投票模块来组合所有 DrosoNet 的输出以得出最终的位置预测结果，该方法在处理外观变化和视点变化时表现优异，并且能够以计算复杂方法在线推断时间的一小部分来竞争一些基准数据集。