  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
driven by different daily illumination, seasonal weather variations and diverse
viewpoints. Currently, no single VPR technique excels in every environmental
condition, each exhibiting unique benefits and shortcomings, and therefore
combining multiple techniques can achieve more reliable VPR performance.
Present multi-method approaches either rely on online ground-truth information,
which is often not available, or on brute-force technique combination,
potentially lowering performance with high variance technique sets. Addressing
these shortcomings, we propose a VPR system dubbed Multi-Sequential Information
Consistency (MuSIC) which leverages sequential information to select the most
cohesive technique on an online per-frame basis. For each technique in a set,
MuSIC computes their respective sequential consistencies by analysing the
frame-to-frame continuity of their top match candidates, which are then
directly compared to select the optimal technique for the current query image.
The use of sequential information to select between VPR methods results in an
overall VPR performance increase across different benchmark datasets, while
avoiding the need for extra ground-truth of the runtime environment.
