Adding Cues to Binary Feature Descriptors for Visual Place Recognition

TL;DR

This paper introduces a method to enhance binary feature descriptors with embedded cues for improved visual place recognition, enabling better matching while maintaining compatibility with existing comparison procedures.

Contribution

It proposes a novel embedding technique for continuous and selector cues into binary descriptors, supporting Hamming distance, and demonstrates its effectiveness through applications and extensive evaluations.

Findings

Improved accuracy in visual place recognition tasks.

Compatibility with existing binary descriptor comparison methods.

Robust performance across multiple benchmark datasets.

Abstract

In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.