Improving Visual Place Recognition Performance by Maximising Complementarity

TL;DR

This paper systematically investigates the complementarity of state-of-the-art visual place recognition methods, proposing a framework to identify optimal combinations that enhance recognition performance.

Contribution

It introduces a novel framework using a McNemar's test-like approach to quantify and leverage the complementarity of VPR methods for improved accuracy.

Findings

Identified complementary pairs of VPR methods with improved combined performance.

Provided bounds for the maximum achievable performance through method combination.

Demonstrated the framework on multiple datasets and methods, showing significant potential.

Abstract

Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information. Many attempts to improve the performance of VPR methods have been made in the literature. One approach that has received attention recently is the multi-process fusion where different VPR methods run in parallel and their outputs are combined in an effort to achieve better performance. The multi-process fusion, however, does not have a well-defined criterion for selecting and combining different VPR methods from a wide range of available options. To the best of our knowledge, this paper investigates the complementarity of state-of-the-art VPR methods systematically for the first time and identifies those combinations which can result in better performance. The paper presents a well-defined framework which acts as a sanity check to find the complementarity between two techniques by utilising a McNemar's test-like approach. The framework allows estimation of upper and lower complementarity bounds for the VPR techniques to be combined, along with an estimate of maximum VPR performance that may be achieved. Based on this framework, results are presented for eight state-of-the-art VPR methods on ten widely-used VPR datasets showing the potential of different combinations of techniques for achieving better performance.