Place recognition: An Overview of Vision Perspective

TL;DR

This paper provides a comprehensive overview of visual place recognition, discussing traditional methods and recent CNN-based approaches, highlighting challenges and future directions in the field.

Contribution

It offers a detailed survey of place recognition techniques, emphasizing the evolution from traditional image descriptors to deep learning methods.

Findings

Traditional image retrieval methods form the basis of place recognition.

CNN-based approaches have significantly improved recognition accuracy.

Challenges remain in achieving long-term robustness across diverse conditions.

Abstract

Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of wisdom 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 system, We start with traditional image description methodology developed in the past, which exploit techniques from 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 system, i.e., convolutional neural networks (CNNs). Thus we then introduce recent progress of visual place recognition system based on CNNs to automatically learn better image representations for places. Eventually, we close with discussions and future work of place recognition.