The University of Southampton
University of Southampton Institutional Repository

A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes

A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes
A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes
his article presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods.
Convolutional neural network (CNN), feature encoding, robot localization, vector of locally aggregated descriptors (VLADs), visual place recognition (VPR)
1552-3098
561-569
Khaliq, Ahmad
d307e38a-904f-4a24-9071-004e3b9bfeca
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Chen, Zetao
6ce5c79e-79d4-455b-85e6-7be7a84de284
Milford, Michael
9edf5ef3-4a6a-4d05-aec2-6146c00cd407
McDonald-Maier, Klaus
4429a771-384b-4cc6-8d45-1813c3792939
Khaliq, Ahmad
d307e38a-904f-4a24-9071-004e3b9bfeca
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Chen, Zetao
6ce5c79e-79d4-455b-85e6-7be7a84de284
Milford, Michael
9edf5ef3-4a6a-4d05-aec2-6146c00cd407
McDonald-Maier, Klaus
4429a771-384b-4cc6-8d45-1813c3792939

Khaliq, Ahmad, Ehsan, Shoaib, Chen, Zetao, Milford, Michael and McDonald-Maier, Klaus (2019) A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes. IEEE Transactions on Robotics, 36 (2), 561-569. (doi:10.1109/TRO.2019.2956352).

Record type: Article

Abstract

his article presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods.

This record has no associated files available for download.

More information

Published date: 27 December 2019
Keywords: Convolutional neural network (CNN), feature encoding, robot localization, vector of locally aggregated descriptors (VLADs), visual place recognition (VPR)

Identifiers

Local EPrints ID: 478921
URI: http://eprints.soton.ac.uk/id/eprint/478921
ISSN: 1552-3098
PURE UUID: a09f2dc0-31a5-4280-b645-42f297ba9ace
ORCID for Shoaib Ehsan: ORCID iD orcid.org/0000-0001-9631-1898

Catalogue record

Date deposited: 13 Jul 2023 16:52
Last modified: 17 Mar 2024 04:16

Export record

Altmetrics

Contributors

Author: Ahmad Khaliq
Author: Shoaib Ehsan ORCID iD
Author: Zetao Chen
Author: Michael Milford
Author: Klaus McDonald-Maier

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×