  Visual place recognition (VPR) is the problem of recognising a previously
visited location using visual information. Many attempts to improve the
performance of VPR methods have been made in the literature. One approach that
has received attention recently is the multi-process fusion where different VPR
methods run in parallel and their outputs are combined in an effort to achieve
better performance. The multi-process fusion, however, does not have a
well-defined criterion for selecting and combining different VPR methods from a
wide range of available options. To the best of our knowledge, this paper
investigates the complementarity of state-of-the-art VPR methods systematically
for the first time and identifies those combinations which can result in better
performance. The paper presents a well-defined framework which acts as a sanity
check to find the complementarity between two techniques by utilising a
McNemar's test-like approach. The framework allows estimation of upper and
lower complementarity bounds for the VPR techniques to be combined, along with
an estimate of maximum VPR performance that may be achieved. Based on this
framework, results are presented for eight state-of-the-art VPR methods on ten
widely-used VPR datasets showing the potential of different combinations of
techniques for achieving better performance.
