mlsanityData to 7 Oct 2026

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

arXiv 2103.01994 ↗First posted 2 Mar 2021cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 60 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

16 of 60 references.

References · 16

  1. 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
  2. 2014CNN Features off-the-shelf: an Astounding Baseline for Recognitioncited by 316
  3. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  4. 2020VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Changecited by 101
  5. 2017Deep Learning Features at Scale for Visual Place Recognitioncited by 96
  6. 2015On the Performance of ConvNet Features for Place Recognitioncited by 72
  7. 2018A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changescited by 53
  8. 2014Convolutional Neural Network-based Place Recognitioncited by 32
  9. 2018Lightweight Unsupervised Deep Loop Closurecited by 32
  10. 2020Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environmentscited by 15
  11. 2021Improving Visual Place Recognition Performance by Maximising Complementaritycited by 15
  12. 2019Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditionscited by 13
4 more
  1. 2020Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representationscited by 10
  2. 2019Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?cited by 9
  3. 2020DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognitioncited by 5
  4. 2020ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environmentscited by 3

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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.