A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments
This addresses the problem of reliable robot navigation and localization in varying conditions for robotics applications, representing an incremental improvement by optimizing ensemble efficiency.
The paper tackles the challenge of visual place recognition (VPR) in changing environments by proposing A-MuSIC, an adaptive ensemble system that dynamically selects techniques based on runtime performance analysis, achieving state-of-the-art VPR performance across benchmark datasets while maintaining computational load comparable to individual techniques.
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.