Visual Place Recognition has been the subject of many endeavours utilizing
different ensemble approaches to improve VPR performance. Ideas like
multi-process fusion, Fly-Inspired Voting Units, SwitchHit or Switch-Fuse
involve combining different VPR techniques together, utilizing different
strategies. However, a major aspect often common to many of these strategies is
voting. Voting is an extremely relevant topic to explore in terms of its
application and significance for any ensemble VPR setup. This paper analyses
several voting schemes to maximise the place detection accuracy of a VPR
ensemble set up and determine the optimal voting schemes for selection. We take
inspiration from a variety of voting schemes that are widely employed in fields
such as politics and sociology and it is evident via empirical data that the
selection of the voting method influences the results drastically. The paper
tests a wide variety of voting schemes to present the improvement in the VPR
results for several data sets. We aim to determine whether a single optimal
voting scheme exists or, much like in other fields of research, the selection
of a voting technique is relative to its application and environment. We
propose a ranking of these different voting methods from best to worst which
allows for better selection. While presenting our results in terms of voting
method's performance bounds, in form of radar charts, PR curves to showcase the
difference in performance and a comparison methodology using a McNemar test
variant to determine the statistical significance of the differences. This test
is performed to further confirm the reliability of outcomes and draw
comparisons for better and informed selection a voting technique.

本文通过分析不同的投票方案，提出了一种最大化视觉地点识别 (VPR) 集合设置的地点检测准确性并确定最佳投票方案的方法。通过在多个数据集上测试各种投票方案，我们旨在确定是否存在单一的最佳投票方案，或者选择投票技术是相对于其应用和环境的。同时，通过雷达图、PR 曲线和 McNemar 测试等方法，展示了不同投票方法的性能边界，并通过比较方法确定差异的统计显著性，以便进行更好的选择。