Paper Info
Title
Improving Visual Place Recognition Performance By Maximising Complementarity
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 letter 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.
Year
DOI
Venue
2021
10.1109/LRA.2021.3088779
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Hidden Markov models, Image recognition, Visualization, Stacking, Sensors, Measurement, Image sensors, Visual place recognition, localization, navigation, complementarity, multi-process fusion
Journal
6
Issue
ISSN
Citations 
3
2377-3766
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Maria Waheed100.34
Michael Milford2122184.09
Klaus D. McDonald-Maier332754.43
Shoaib Ehsan411024.43