Location retrieval using visible landmarks based qualitative place signatures

• URL: http://arxiv.org/abs/2208.00783v1
• Date: Tue, 26 Jul 2022 13:57:49 GMT
• Title: Location retrieval using visible landmarks based qualitative place signatures
• Authors: Lijun Wei, Valerie Gouet-Brunet, Anthony Cohn
• Abstract summary: A qualitative location retrieval method is proposed in this work by describing locations/places using qualitative place signatures (QPS) After dividing the space into place cells each with individual signatures attached, a coarse-to-fine location retrieval method is proposed to efficiently identify the possible location(s) of viewers based on their qualitative observations.
• Score: 0.7119463843130092
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 the observations with a certain form of representation of the environment. Existing methods generally require precise measurement and storage of the observed environment features, which may not always be robust due to the change of season, viewpoint, occlusion, etc. They are also challenging to scale up and may not be applicable for humans due to the lack of measuring/imaging devices. Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a qualitative location retrieval method is proposed in this work by describing locations/places using qualitative place signatures (QPS), defined as the perceived spatial relations between ordered pairs of co-visible landmarks from viewers' perspective. After dividing the space into place cells each with individual signatures attached, a coarse-to-fine location retrieval method is proposed to efficiently identify the possible location(s) of viewers based on their qualitative observations. The usability and effectiveness of the proposed method were evaluated using openly available landmark datasets, together with simulated observations by considering the possible perception error.

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