Kinect@Home: A Crowdsourced RGB-D Dataset

  • Rasmus Göransson
  • Alper Aydemir
  • Patric Jensfelt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

RGB-D Dataset Reconstruction SLAM Benchmark 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rasmus Göransson
    • 1
  • Alper Aydemir
    • 2
  • Patric Jensfelt
    • 1
  1. 1.Centre for Autonomous SystemsKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Computer Vision Group, Jet Propulsion Laboratory, NASALos AngelesUSA

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