On the Study of Data Augmentation for Visual Place Recognition

In the field of robotics engineering and autonomous driving vehicles, precise estimation of positions through visual place recognition (VPR) is crucial not only for reducing localization errors caused by visual odometry but also for preventing the creation of ambiguous maps in unfamiliar environments. Despite numerous research efforts aimed at improving VPR performance by addressing challenges such as illumination variation, occlusions, and dynamic objects, contemporary approaches have primarily focused on model-based methods, with limited attention given to data augmentation (DA) methods. Therefore, there is a need to investigate the impact of DA on the generalization ability of VPR. To achieve this objective, this study compares VPR learning approaches, conducts a comprehensive empirical analysis, and presents crucial insights. The results of this study can provide useful guidance for the design of future VPR systems and contribute to the advancement of computer vision and robotics research.