A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition

TL;DR

This paper introduces a novel unsupervised method for visual place recognition that estimates environment- and place-specific utility of visual features, leading to improved accuracy and efficiency on benchmark datasets.

Contribution

It presents a dual utility estimation approach using contrastive learning for VLAD clusters, enhancing VPR performance and interpretability.

Findings

Achieves state-of-the-art results on benchmark datasets.

Reduces storage and computation requirements.

Finer-grained utility categorization improves recognition accuracy.

Abstract

Visual Place Recognition (VPR) approaches have typically attempted to match places by identifying visual cues, image regions or landmarks that have high ``utility'' in identifying a specific place. But this concept of utility is not singular - rather it can take a range of forms. In this paper, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues `specific' to an environment, and to a particular place. We employ contrastive learning principles to estimate both the environment- and place-specific utility of Vector of Locally Aggregated Descriptors (VLAD) clusters in an unsupervised manner, which is then used to guide local feature matching through keypoint selection. By combining these two utility measures, our approach achieves state-of-the-art performance on three challenging benchmark datasets, while simultaneously reducing the required storage and compute time. We provide further analysis demonstrating that unsupervised cluster selection results in semantically meaningful results, that finer grained categorization often has higher utility for VPR than high level semantic categorization (e.g. building, road), and characterise how these two utility measures vary across different places and environments. Source code is made publicly available at https://github.com/Nik-V9/HEAPUtil.