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An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry
In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301040/ https://www.ncbi.nlm.nih.gov/pubmed/37374682 http://dx.doi.org/10.3390/mi14061097 |
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author | Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin |
author_facet | Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin |
author_sort | Zheng, Ping |
collection | PubMed |
description | In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost. |
format | Online Article Text |
id | pubmed-10301040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103010402023-06-29 An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin Micromachines (Basel) Article In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost. MDPI 2023-05-23 /pmc/articles/PMC10301040/ /pubmed/37374682 http://dx.doi.org/10.3390/mi14061097 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title | An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title_full | An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title_fullStr | An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title_full_unstemmed | An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title_short | An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry |
title_sort | indoor visual positioning method with 3d coordinates using built-in smartphone sensors based on epipolar geometry |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301040/ https://www.ncbi.nlm.nih.gov/pubmed/37374682 http://dx.doi.org/10.3390/mi14061097 |
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