A Hierarchical Dual Model of Environment- And Place-Specific Utility for Visual Place Recognition
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Description
Visual Place Recognition (VPR) approaches have typically attempted to match places by identifying visual cues, image regions or landmarks that have high 'utility' in identifying a specific place. But this concept of utility is not singular - rather it can take a range of forms. In this letter, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues 'specific' to an environment, and to a particular place. We employ contrastive learning principles to estimate both the environment- and place-specific utility of Vector of Locally Aggregated Descriptors (VLAD) clusters in an unsupervised manner, which is then used to guide local feature matching through keypoint selection. By combining these two utility measures, our approach achieves state-of-the-art performance on three challenging benchmark datasets, while simultaneously reducing the required storage and compute time. We provide further analysis demonstrating that unsupervised cluster selection results in semantically meaningful results, that finer grained categorization often has higher utility for VPR than high level semantic categorization (e.g. building, road), and characterise how these two utility measures vary across different places and environments. Source code is made publicly available at https://github.com/Nik-V9/HEAPUtil.
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ID Code: | 213701 | ||||
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Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
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Measurements or Duration: | 8 pages | ||||
Keywords: | deep learning methods, Localization, semantic scene understanding, visual place recognition | ||||
DOI: | 10.1109/LRA.2021.3096751 | ||||
ISSN: | 2377-3766 | ||||
Pure ID: | 99387681 | ||||
Divisions: | Current > Research Centres > Centre for Robotics Current > Research Centres > Centre for Future Mobility/CARRSQ Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > QUT Faculties and Divisions > Faculty of Health |
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Funding Information: | Manuscript received February 24, 2021; accepted June 24, 2021. Date of publication July 14, 2021; date of current version July 27, 2021. This work was supported by the Queensland University of Technology (QUT) through the Centre for Robotics. This letter was recommended for publication by Editor Sven Behnke upon evaluation of the Associate Editor and reviewers’ comments. (Corresponding author: Sourav Garg.) Nikhil Varma Keetha is with the Indian Institute of Technology (ISM) Dhan-bad, Hyderabad 500035, India (e-mail: keethanikhil@gmail.com). | ||||
Copyright Owner: | 2021 IEEE | ||||
Copyright Statement: | Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||
Deposited On: | 06 Oct 2021 05:58 | ||||
Last Modified: | 08 Jun 2024 10:44 |
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