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Bekiroglu Y, Björkman M, Zarzar Gandler G, et al. Visual and tactile 3D point cloud data from real robots for shape modeling and completion. Data Brief. 2020;30:105335doi: 10.1016/j.dib.2020.105335.
Bekiroglu, Y., Björkman, M., Zarzar Gandler, G., Exner, J., Ek, C. H., & Kragic, D. (2020). Visual and tactile 3D point cloud data from real robots for shape modeling and completion. Data in brief, 30105335. https://doi.org/10.1016/j.dib.2020.105335
Bekiroglu, Yasemin, et al. "Visual and tactile 3D point cloud data from real robots for shape modeling and completion." Data in brief vol. 30 (2020): 105335. doi: https://doi.org/10.1016/j.dib.2020.105335
Bekiroglu Y, Björkman M, Zarzar Gandler G, Exner J, Ek CH, Kragic D. Visual and tactile 3D point cloud data from real robots for shape modeling and completion. Data Brief. 2020 Feb 26;30:105335. doi: 10.1016/j.dib.2020.105335. eCollection 2020 Jun. PMID: 32258263; PMCID: PMC7125316.
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Data Brief. 2020 Feb 26;30:105335. doi: 10.1016/j.dib.2020.105335. eCollection 2020 Jun.

Visual and tactile 3D point cloud data from real robots for shape modeling and completion.

Data in brief

Yasemin Bekiroglu, Mårten Björkman, Gabriela Zarzar Gandler, Johannes Exner, Carl Henrik Ek, Danica Kragic

Affiliations

  1. University of Birmingham, UK.
  2. KTH Royal Institute of Technology, Sweden.
  3. Peltarion, Sweden.
  4. University of Bristol, UK.

PMID: 32258263 PMCID: PMC7125316 DOI: 10.1016/j.dib.2020.105335

Abstract

Representing 3D geometry for different tasks, e.g. rendering and reconstruction, is an important goal in different fields, such as computer graphics, computer vision and robotics. Robotic applications often require perception of object shape information extracted from sensory data that can be noisy and incomplete. This is a challenging task and in order to facilitate analysis of new methods and comparison of different approaches for shape modeling (e.g. surface estimation), completion and exploration, we provide real sensory data acquired from exploring various objects of different complexities. The dataset includes visual and tactile readings in the form of 3D point clouds obtained using two different robot setups that are equipped with visual and tactile sensors. During data collection, the robots touch the experiment objects in a predefined manner at various exploration configurations and gather visual and tactile points in the same coordinate frame based on calibration between the robots and the used cameras. The goal of this exhaustive exploration procedure is to sense unseen parts of the objects which are not visible to the cameras, but can be sensed via tactile sensors activated at touched areas. The data was used for shape completion and modeling via Implicit Surface representation and Gaussian-Process-based regression, in the work "Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration" [3], and also used partially in "Enhancing visual perception of shape through tactile glances" [4], both studying efficient exploration of objects to reduce number of touches.

© 2020 The Authors. Published by Elsevier Inc.

Keywords: Gaussian process; Implicit surface; Point cloud; Regression; Shape completion; Shape modeling; Tactile sensing; Visual depth sensing

References

  1. Science. 2019 Jun 21;364(6446): - PubMed

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