Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition

TL;DR

This paper introduces a training-free, real-time quality prediction method for visual place recognition that enhances sequence matching performance and is adaptable to various techniques and datasets.

Contribution

It presents a novel, training-free quality prediction approach and a weighted sequence matching method that improve localization accuracy in visual place recognition systems.

Findings

Improved precision at high-precision/low-recall points

System is lightweight and runs in real-time

Effective across multiple datasets and VPR techniques

Abstract

While substantial progress has been made in the absolute performance of localization and Visual Place Recognition (VPR) techniques, it is becoming increasingly clear from translating these systems into applications that other capabilities like integrity and predictability are just as important, especially for safety- or operationally-critical autonomous systems. In this research we present a new, training-free approach to predicting the likely quality of localization estimates, and a novel method for using these predictions to bias a sequence-matching process to produce additional performance gains beyond that of a naive sequence matching approach. Our combined system is lightweight, runs in real-time and is agnostic to the underlying VPR technique. On extensive experiments across four datasets and three VPR techniques, we demonstrate our system improves precision performance, especially at the high-precision/low-recall operating point. We also present ablation and analysis identifying the performance contributions of the prediction and weighted sequence matching components in isolation, and the relationship between the quality of the prediction system and the benefits of the weighted sequential matcher.