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Improving Visual Place Recognition Performance by Maximising Complementarity

Waheed, Maria and Milford, Michael and McDonald-Maier, Klaus and Ehsan, Shoaib (2021) 'Improving Visual Place Recognition Performance by Maximising Complementarity.' IEEE Robotics and Automation Letters, 6 (3). pp. 5976-5983. ISSN 2377-3766

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Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information. Many attempts to improve the performance of VPR methods have been made in the literature. One approach that has received attention recently is the multi-process fusion where different VPR methods run in parallel and their outputs are combined in an effort to achieve better performance. The multi-process fusion, however, does not have a well-defined criterion for selecting and combining different VPR methods from a wide range of available options. To the best of our knowledge, this paper investigates the complementarity of state-of-the-art VPR methods systematically for the first time and identifies those combinations which can result in better performance. The letter presents a well-defined framework which acts as a sanity check to find the complementarity between two techniques by utilising a McNemar's test-like approach. The framework allows estimation of upper and lower complementarity bounds for the VPR techniques to be combined, along with an estimate of maximum VPR performance that may be achieved. Based on this framework, results are presented for eight state-of-the-art VPR methods on ten widely-used VPR datasets showing the potential of different combinations of techniques for achieving better performance.

Item Type: Article
Uncontrolled Keywords: Visual place recognition; localization; navigation; complementarity; multi-process fusion
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: 13 Jan 2022 12:06
Last Modified: 15 Jan 2022 01:36

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