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Computer Science > Computer Vision and Pattern Recognition

arXiv:2305.05776 (cs)
[Submitted on 9 May 2023]

Title:Visual Place Recognition with Low-Resolution Images

Authors:Mihnea-Alexandru Tomita, Bruno Ferrarini, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan
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Abstract:Images incorporate a wealth of information from a robot's surroundings. With the widespread availability of compact cameras, visual information has become increasingly popular for addressing the localisation problem, which is then termed as Visual Place Recognition (VPR). While many applications use high-resolution cameras and high-end systems to achieve optimal place-matching performance, low-end commercial systems face limitations due to resource constraints and relatively low-resolution and low-quality cameras. In this paper, we analyse the effects of image resolution on the accuracy and robustness of well-established handcrafted VPR pipelines. Handcrafted designs have low computational demands and can adapt to flexible image resolutions, making them a suitable approach to scale to any image source and to operate under resource limitations. This paper aims to help academic researchers and companies in the hardware and software industry co-design VPR solutions and expand the use of VPR algorithms in commercial products.
Comments: The paper has been accepted for presentation at the Active Methods in Autonomous Navigation Workshop, part of the 2023 International Conference on Robotics and Automation (ICRA)
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2305.05776 [cs.CV]
  (or arXiv:2305.05776v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2305.05776

Submission history

From: Mihnea-Alexandru Tomita [view email]
[v1] Tue, 9 May 2023 21:34:38 UTC (4,245 KB)
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