Journal
IEEE ACCESS
Volume 10, Issue -, Pages 81974-81987Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3196389
Keywords
Filtering; Visualization; Navigation; Convolutional neural networks; Lighting; Image matching; Electronic mail; Sequence-based filtering; visual localization; visual place recognition
Categories
Funding
- U.K. Engineering and Physical Sciences Research Council [EP/R02572X/1, EP/P017487/1]
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This paper conducts an in-depth investigation into the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering. It analyzes the trade-offs, properties, and limitations of different combinations and demonstrates the benefits of sequence-based filtering for VPR accuracy and time efficiency.
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.
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