Visual Place Recognition: A Tutorial

Abstract

is an essential capability for mobile robots, enabling them to build a comprehensive representation of their environment and interact with the environment effectively towards a goal. A rapidly growing field of research in this area is Visual Place Recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images. The volume of published research on VPR has shown a significant and continuous growth over the years, from two papers with "visual place recognition" and seven papers with "place recognition" in the title in 2006, compared to 65 and 163 papers, respectively, in 2022 1 . A number of survey and benchmarking papers have discussed the challenges, open questions, and achievements in the field of VPR [1], [2], [3], [4], [5], [6].This present work is the first tutorial paper on visual place recognition. It unifies the terminology of VPR and complements prior research in two important directions:1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a generic algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed. 2) As a contribution for researchers acquainted with the VPR problem, it examines the intricacies of different VPR problem types regarding input (database or query set), data processing (online or offline) and output (one or multiple matches per query image). The tutorial also discusses the subtleties behind the evaluation of VPR algorithms, e.g., the evaluation of a VPR system that has to find all matching database images per query, as opposed to just a single match.

Inpainting Semantic and Depth Features to Improve Visual Place Recognition in the Wild

“…O NE of the problems that recently gained popularity in computer vision is the visual place recognition [1]- [5]. This task aims to find a location where an image was taken based only on the image itself.…”

A comparison of visual place recognition methods using a mobile robot in an indoor environment

“…We see that over the past few decades, methods transitioned from traditional handcrafted feature-based methods to more recently learned feature-based methods. In Schubert et al [4] they discuss state-of-the-art deep learning methods that can be used to address challenges in VPR. It provides an introduction to how VPR is implemented and evaluates different approaches.…”