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Visual place recognition (VPR) is an essential component of robot navigationand localization systems that allows them to identify a place using only imagedata. VPR is challenging due to the significant changes in a place's appearanceunder different illumination throughout the day, with seasonal weather and whenobserved from different viewpoints. Currently, no single VPR technique excelsin every environmental condition, each exhibiting unique benefits andshortcomings. As a result, VPR systems combining multiple techniques achievemore reliable VPR performance in changing environments, at the cost of highercomputational loads. Addressing this shortcoming, we propose an adaptive VPRsystem dubbed Adaptive Multi-Self Identification and Correction (A-MuSIC). Westart by developing a method to collect information of the runtime performanceof a VPR technique by analysing the frame-to-frame continuity of matchedqueries. We then demonstrate how to operate the method on a static ensemble oftechniques, generating data on which techniques are contributing the most forthe current environment. A-MuSIC uses the collected information to both selecta minimal subset of techniques and to decide when a re-selection is requiredduring navigation. A-MuSIC matches or beats state-of-the-art VPR performanceacross all tested benchmark datasets while maintaining its computational loadon par with individual techniques.

A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments