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Indoor visual SLAM dataset with various acquisition modalities

The indoor Visual Simultaneous Localization And Mapping (V-SLAM) dataset with various acquisition modalities has been created to evaluate the impact of acquisition modalities on the Visual SLAM algorithm’s accuracy. The dataset contains different sequences acquired with different modalities, includi...

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Detalles Bibliográficos
Autores principales: El Bouazzaoui, Imad, Rodriguez, Sergio, Vincke, Bastien, El Ouardi, Abdelhafid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552193/
https://www.ncbi.nlm.nih.gov/pubmed/34746344
http://dx.doi.org/10.1016/j.dib.2021.107496
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author El Bouazzaoui, Imad
Rodriguez, Sergio
Vincke, Bastien
El Ouardi, Abdelhafid
author_facet El Bouazzaoui, Imad
Rodriguez, Sergio
Vincke, Bastien
El Ouardi, Abdelhafid
author_sort El Bouazzaoui, Imad
collection PubMed
description The indoor Visual Simultaneous Localization And Mapping (V-SLAM) dataset with various acquisition modalities has been created to evaluate the impact of acquisition modalities on the Visual SLAM algorithm’s accuracy. The dataset contains different sequences acquired with different modalities, including RGB, IR, and depth images in passive stereo and active stereo modes. Each sequence is associated with a reference trajectory constructed with an Structure From Motion (SFM) and Multi View Stereo (MVS) library for comparison. Data were collected using an intrinsically calibrated Intel RealSense D435i camera. The RGB/IR and depth data are spatially aligned, and the stereo images are rectified. The dataset includes various areas, some with low brightness, with changes in brightness, wide, narrow and texture.
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spelling pubmed-85521932021-11-04 Indoor visual SLAM dataset with various acquisition modalities El Bouazzaoui, Imad Rodriguez, Sergio Vincke, Bastien El Ouardi, Abdelhafid Data Brief Data Article The indoor Visual Simultaneous Localization And Mapping (V-SLAM) dataset with various acquisition modalities has been created to evaluate the impact of acquisition modalities on the Visual SLAM algorithm’s accuracy. The dataset contains different sequences acquired with different modalities, including RGB, IR, and depth images in passive stereo and active stereo modes. Each sequence is associated with a reference trajectory constructed with an Structure From Motion (SFM) and Multi View Stereo (MVS) library for comparison. Data were collected using an intrinsically calibrated Intel RealSense D435i camera. The RGB/IR and depth data are spatially aligned, and the stereo images are rectified. The dataset includes various areas, some with low brightness, with changes in brightness, wide, narrow and texture. Elsevier 2021-10-19 /pmc/articles/PMC8552193/ /pubmed/34746344 http://dx.doi.org/10.1016/j.dib.2021.107496 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
El Bouazzaoui, Imad
Rodriguez, Sergio
Vincke, Bastien
El Ouardi, Abdelhafid
Indoor visual SLAM dataset with various acquisition modalities
title Indoor visual SLAM dataset with various acquisition modalities
title_full Indoor visual SLAM dataset with various acquisition modalities
title_fullStr Indoor visual SLAM dataset with various acquisition modalities
title_full_unstemmed Indoor visual SLAM dataset with various acquisition modalities
title_short Indoor visual SLAM dataset with various acquisition modalities
title_sort indoor visual slam dataset with various acquisition modalities
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552193/
https://www.ncbi.nlm.nih.gov/pubmed/34746344
http://dx.doi.org/10.1016/j.dib.2021.107496
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