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An Efficient and Scalable Collection of Fly-Inspired Voting Units for Visual Place Recognition in Changing Environments

Arcanjo, Bruno and Ferrarini, Bruno and Milford, Michael and McDonald-Maier, Klaus D and Ehsan, Shoaib (2022) 'An Efficient and Scalable Collection of Fly-Inspired Voting Units for Visual Place Recognition in Changing Environments.' IEEE Robotics and Automation Letters, 7 (2). pp. 2527-2534. ISSN 2377-3766

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Abstract

State-of-the-art visual place recognition performance is currently being achieved utilizing deep learning based approaches. Despite the recent efforts in designing lightweight convolutional neural network based models, these can still be too expensive for the most hardware restricted robot applications. Low-overhead visual place recognition techniques would not only enable platforms equipped with low-end, cheap hardware but also reduce computation on more powerful systems, allowing these resources to be allocated for other navigation tasks. In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations. Our first contribution is DrosoNet, an exceptionally compact model inspired by the odor processing abilities of the fruit fly, Drosophila melanogaster. Our second and main contribution is a voting mechanism that leverages multiple small and efficient classifiers to achieve more robust and consistent visual place recognition compared to a single one. We use DrosoNet as the baseline classifier for the voting mechanism and evaluate our models on five benchmark datasets, assessing moderate to extreme appearance changes and small to moderate viewpoint variations. We then compare the proposed algorithms to state-of-the-art methods, both in terms ofarea under the precision-recall curve results and computational efficiency.

Item Type: Article
Uncontrolled Keywords: Vision-based navigation; localization; bioinspired robot learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Jun 2022 15:52
Last Modified: 27 Jun 2022 15:52
URI: http://repository.essex.ac.uk/id/eprint/33070

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