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

TL;DR

A-MuSIC is an adaptive ensemble system for visual place recognition that dynamically selects the most effective techniques during navigation, improving accuracy in changing environments without increasing computational costs.

Contribution

It introduces an adaptive method to select and reselect VPR techniques based on runtime performance, enhancing reliability in diverse environmental conditions.

Findings

Matches or exceeds state-of-the-art VPR performance

Maintains computational load comparable to single techniques

Effectively adapts to environmental changes during navigation

Abstract

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.