Visual Place Recognition: A Tutorial
원문 초록
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.
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Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation
Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping
Visual Re-Ranking with Non-Visual Side Information
Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
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