Towards A Deep Insight into Landmark-based Visual Place Recognition: Methodology and Practice

TL;DR

This paper investigates landmark-based visual place recognition, analyzing how landmark scale and overlap affect performance, and proposes a dense sampling approach that improves recognition accuracy over traditional object proposal methods.

Contribution

The paper provides a comprehensive analysis of landmark generation strategies and introduces a dense sampling scheme for more effective landmark-based place recognition.

Findings

Landmark scale and overlap significantly influence recognition performance.

Dense sampling of landmarks outperforms traditional object proposal methods.

Multi-scale fusion enhances the accuracy of place recognition.

Abstract

In this paper, we address the problem of landmark-based visual place recognition. In the state-of-the-art method, accurate object proposal algorithms are first leveraged for generating a set of local regions containing particular landmarks with high confidence. Then, these candidate regions are represented by deep features and pairwise matching is performed in an exhaustive manner for the similarity measure. Despite its success, conventional object proposal methods usually produce massive landmark-dependent image patches exhibiting significant distribution variance in scale and overlap. As a result, the inconsistency in landmark distributions tends to produce biased similarity between pairwise images yielding the suboptimal performance. In order to gain an insight into the landmark-based place recognition scheme, we conduct a comprehensive study in which the influence of landmark scales and the proportion of overlap on the recognition performance is explored. More specifically, we thoroughly study the exhaustive search based landmark matching mechanism, and thus derive three-fold important observations in terms of the beneficial effect of specific landmark generation strategies. Inspired by the above observations, a simple yet effective dense sampling based scheme is presented for accurate place recognition in this paper. Different from the conventional object proposal strategy, we generate local landmarks of multiple scales with uniform distribution from entire image by dense sampling, and subsequently perform multi-scale fusion on the densely sampled landmarks for similarity measure. The experimental results on three challenging datasets demonstrate that the recognition performance can be significantly improved by our efficient method in which the landmarks are appropriately produced for accurate pairwise matching.