Sequence-Based Filtering for Visual Route-Based Navigation

Analyzing the Benefits, Trade-Offs and Design Choices

Journal Article (2022)
Author(s)

Mihnea Alexandru Tomita (University of Essex)

M. Zaffar (TU Delft - Intelligent Vehicles)

Bruno Ferrarini (Queensland University of Technology)

Michael Milford (University of Essex)

Klaus McDonald-Maier (University of Essex)

Shoaib Ehsan (University of Essex)

Research Group
Intelligent Vehicles
Copyright
© 2022 Mihnea Alexandru Tomita, M. Zaffar, Bruno Ferrarini, Michael J. Milford, Klaus McDonald-Maier, Shoaib Ehsan
DOI related publication
https://doi.org/10.1109/ACCESS.2022.3196389
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Mihnea Alexandru Tomita, M. Zaffar, Bruno Ferrarini, Michael J. Milford, Klaus McDonald-Maier, Shoaib Ehsan
Research Group
Intelligent Vehicles
Volume number
10
Pages (from-to)
81974-81987
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Abstract

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. The experiments conducted in this study demonstrate the benefits of sequence-based filtering over the single-frame-based approach using various VPR techniques. We found that applying sequence-based filtering to a lightweight descriptor can enable higher VPR accuracy than state-of-the-art methods such as NetVLAD, while running in shorter time. For example, matching a sequence of 16 images, CALC descriptor outperforms NetVLAD on Campus Loop dataset while taking about 22% less time to perform VPR.