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

TL;DR

This paper investigates how sequence-based filtering improves visual place recognition performance in route-based navigation, analyzing trade-offs, design choices, and the impact of sequence length across various methods and datasets.

Contribution

It provides a systematic analysis of the performance benefits and computational trade-offs of sequence-based filtering on top of single-frame VPR methods, filling a research gap.

Findings

Sequence filtering enhances place recognition performance variably.

Trade-offs exist between sequence length, accuracy, and computational cost.

Different method combinations offer distinct performance and efficiency balances.

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.