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Intelligent Autonomous Systems 13 pp 843–858Cite as

Kinect@Home: A Crowdsourced RGB-D Dataset

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

Algorithms for 3D localization, mapping, and reconstruction are getting increasingly mature. It is time to also make the datasets on which they are tested more realistic to reflect the conditions in the homes of real people. Today algorithms are tested on data gathered in the lab or at best in a few places, and almost always by the people that designed the algorithm. In this paper, we present the first RGB-D dataset from the crowdsourced data collection project Kinect@Home and perform an initial analysis of it. The dataset contains 54 recordings with a total of approximately 45 min of RGB-D video. We present a comparison of two different pose estimation methods, the Kinfu algorithm and a key point-based method, to show how this dataset can be used even though it is lacking ground truth. In addition, the analysis highlights the different characteristics and error modes of the two methods and shows how challenging data from the real world is.

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Notes

  1. 1.

    http://www.cas.kth.se/data/kah.

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Acknowledgments

The authors would like to thank Daniel Henell, Max Roth, and Mihai Damaschin.

Author information

Authors and Affiliations

  1. Centre for Autonomous Systems, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden

    Rasmus Göransson & Patric Jensfelt

  2. Computer Vision Group, Jet Propulsion Laboratory, NASA, 91109, Los Angeles, CA, USA

    Alper Aydemir

Authors
  1. Rasmus Göransson
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  2. Alper Aydemir
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  3. Patric Jensfelt
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Corresponding author

Correspondence to Rasmus Göransson .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Göransson, R., Aydemir, A., Jensfelt, P. (2016). Kinect@Home: A Crowdsourced RGB-D Dataset. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_61

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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