Place Recognition: An Overview of Vision Perspective

Abstract

Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place-recognition literature. Since condition-invariant and viewpoint-invariant features are essential factors to long-term robust visual place-recognition systems, we start with traditional image-description methodology developed in the past, which exploits techniques from the image-retrieval field. Recently, the rapid advances of related fields, such as object detection and image classification, have inspired a new technique to improve visual place-recognition systems, that is, convolutional neural networks (CNNs). Thus, we then introduce the recent progress of visual place-recognition systems based on CNNs to automatically learn better image representations for places. Finally, we close with discussions and mention of future work on place recognition.