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:
Data Article
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
Descripción
Sumario: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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