  Visual place recognition (VPR) is an essential component of robot navigation
and localization systems that allows them to identify a place using only image
data. VPR is challenging due to the significant changes in a place's appearance
under different illumination throughout the day, with seasonal weather and when
observed from different viewpoints. Currently, no single VPR technique excels
in every environmental condition, each exhibiting unique benefits and
shortcomings. As a result, VPR systems combining multiple techniques achieve
more reliable VPR performance in changing environments, at the cost of higher
computational loads. Addressing this shortcoming, we propose an adaptive VPR
system dubbed Adaptive Multi-Self Identification and Correction (A-MuSIC). We
start by developing a method to collect information of the runtime performance
of a VPR technique by analysing the frame-to-frame continuity of matched
queries. We then demonstrate how to operate the method on a static ensemble of
techniques, generating data on which techniques are contributing the most for
the current environment. A-MuSIC uses the collected information to both select
a minimal subset of techniques and to decide when a re-selection is required
during navigation. A-MuSIC matches or beats state-of-the-art VPR performance
across all tested benchmark datasets while maintaining its computational load
on par with individual techniques.
