  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.

Computer Vision and Pattern Recognition