English

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

Computer Vision and Pattern Recognition 2022-09-20 v1

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. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.

Keywords

Cite

@article{arxiv.2103.01994,
  title  = {Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices},
  author = {Mihnea-Alexandru Tomită and Mubariz Zaffar and Michael Milford and Klaus McDonald-Maier and Shoaib Ehsan},
  journal= {arXiv preprint arXiv:2103.01994},
  year   = {2022}
}

Comments

7 pages, currently under-review

R2 v1 2026-06-23T23:40:49.218Z