Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects.
Object detection dataset
Object dimensions estimation
Orientation detection
Pose detection
Primitive object
Sim-to-real transfer
Journal
BMC research notes
ISSN: 1756-0500
Titre abrégé: BMC Res Notes
Pays: England
ID NLM: 101462768
Informations de publication
Date de publication:
28 Jul 2022
28 Jul 2022
Historique:
received:
31
08
2021
accepted:
14
07
2022
entrez:
28
7
2022
pubmed:
29
7
2022
medline:
2
8
2022
Statut:
epublish
Résumé
Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions.
Identifiants
pubmed: 35902920
doi: 10.1186/s13104-022-06155-4
pii: 10.1186/s13104-022-06155-4
pmc: PMC9331585
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
265Subventions
Organisme : University of Tehran Science and Technology Park, (Growth) program
ID : 180/241632
Informations de copyright
© 2022. The Author(s).
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