Visual Place Recognition: A Tutorial

Abstract

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.

IEEE Robotics & Automation Magazine (RAM)

References in corpus (3)

• The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
• Where is your place, Visual Place Recognition?
• General Place Recognition Survey: Towards the Real-world Autonomy Age

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• Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline
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• Towards Revisiting Visual Place Recognition for Joining Submaps in Multimap SLAM