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Document type:
Forschungsdaten
Publication date:
04.06.2024
Responsible:
Vega-Torres, Miguel-A.
Authors:
Vega-Torres, Miguel-A. ; Braun, Alexander; Borrmann, André
Author affiliation:
TUM
Publisher:
TUM
Title:
ConSLAM BIM and GT Poses
Identifier:
doi:10.14459/2024mp1743877
End date of data production:
15.03.2024
Subject area:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; ELT Elektrotechnik; TEC Technik, Ingenieurwissenschaften (allgemein); VER Technik der Verkehrsmittel
Resource type:
Experimente und Beobachtungen / experiments and observations; Statistik und Referenzdaten / statistics and reference data
Other resource types:
Manual creation for the model and automatic for the poses
Data type:
mehrdimensionale Visualisierungen oder Modelle / models
Other data type:
Industry Fundation Classes (IFC) and Revit model, and .TUM files with poses information
Description:
The ConSLAM BIM and GT Poses comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined grounth truth (GT) poses (in TUM format) of the sessions 2, 3, 4 and 5 of the open-access ConSLAM dataset. This dataset can be found here: https://github.com/mac137/ConSLAM
Method of data assessment:
3D geometric and semantic BIM model generated with Autodesk revit and the 6-degrees-of-freedom time-stammed poses resulted from our proposed SLAM2REF method: https://github.com/MigVega/SLAM2REF
Links:
This dataset relates to the publication: https://doi.org/10.1007/s41693-024-00126-w
Key words:
LiDAR; Multi-Session SLAM; Pose-Graph Optimization; Loop Closure; Long-term Mapping; Change Detection; BIM Update; 3D Indoor Localization and Mapping
Technical remarks:
View and download (71 MB total, 7 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1743877):
rsync rsync://m1743877@dataserv.ub.tum.de/m1743877/
Language:
en
Rights:
by-nc, http://creativecommons.org/licenses/by-nc/4.0
Horizon 2020:
This research is part of the INTREPID project funded by EU's Horizon 2020 program (Grant agreement ID: 883345)
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