Wei, L. orcid.org/0000-0002-0422-4963, Gouet-Brunet, V. orcid.org/0000-0003-3666-5146 and Cohn, A.G. orcid.org/0000-0002-7652-8907 (2024) Location retrieval using qualitative place signatures of visible landmarks. International Journal of Geographical Information Science. ISSN 1365-8816
Abstract
Location retrieval based on visual information is to retrieve the location of an agent (e.g. human, robot) or the area they see by comparing their observations with a certain representation of the environment. Existing methods generally treat the problem as a content-based image retrieval problem and have demonstrated promising results in terms of localization accuracy. However, these methods are challenging to scale up due to the volume of reference data involved; and the image descriptions might not be easily understandable/communicable for humans to describe surroundings. Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a coarse-to-fine qualitative location retrieval method is proposed in this work to quickly narrow down the initial location of an agent by exploiting the available information in large-scale open data. This approach describes and indexes a location/place using the perceived qualitative spatial relations between ordered pairs of co-visible landmarks from the perspective of viewers, termed as ‘qualitative place signatures’ (QPS). The usability and effectiveness of the proposed method were evaluated using openly available datasets, together with simulated observations by considering different types perception errors.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s).This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Place recognition, qualitative location, qualitative spatial relation, place descriptor, place signature |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/W003473/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 May 2024 09:22 |
Last Modified: | 23 Jan 2025 15:30 |
Status: | Published online |
Publisher: | Taylor and Francis |
Identification Number: | 10.1080/13658816.2024.2348736 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212965 |
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