Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

TL;DR

This paper provides a comprehensive comparison of 10 recent Visual Place Recognition methods using standardized metrics to evaluate their performance, efficiency, and memory usage, aiming to unify evaluation standards in the field.

Contribution

It introduces a standardized evaluation framework for VPR methods, enabling fair comparison and highlighting strengths and weaknesses of current approaches.

Findings

Different methods excel under various conditions

Trade-offs exist between accuracy, speed, and memory usage

The analysis guides future research towards more balanced VPR solutions

Abstract

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research.