Localization is an essential capability for mobile robots. 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. 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 general-purpose 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, data processing, and output. 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. Practical code examples in Python illustrate to prospective practitioners and researchers how VPR is implemented and evaluated.

翻译：定位是移动机器人的一项基本能力。该领域中一个快速发展的研究方向是视觉位置识别（VPR），即仅基于图像识别出世界中先前访问过的位置的能力。本文是首篇关于视觉位置识别的教程论文，它统一了VPR的术语体系，并在两个重要方向上补充了现有研究：1）为刚进入该领域的新手提供系统性介绍，涵盖VPR问题的形式化表述、通用算法处理流程、VPR方法的评估方法论，以及VPR面临的主要挑战及其应对策略；2）作为对熟悉VPR问题的研究者的贡献，本文详细考察了不同VPR问题类型在输入、数据处理和输出方面的复杂性。教程还讨论了VPR算法评估中的细微之处，例如需找出每个查询的所有匹配数据库图像（而非仅单一匹配）的VPR系统的评估方法。文中提供的Python实际代码示例，向有志于实践的研究人员展示了VPR的实现与评估方式。