Sequence-Based Filtering for Visual Route-Based Navigation: Analyzing the Benefits, Trade-Offs and Design Choices

Mihnea Alexandru Tomita*, M. Zaffar, Bruno Ferrarini, Michael J. Milford, Klaus McDonald-Maier, Shoaib Ehsan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.
Original languageEnglish
Pages (from-to)81974-81987
JournalIEEE Access
Publication statusPublished - 2022


  • sequence-based filtering
  • visual localization
  • visual place recognition


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