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Comparative Evaluation of 3D Pose Estimation of Industrial Objects in RGB Pointclouds

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Computer Vision Systems (ICVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9163))

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Abstract

3D pose estimation is a crucial element for enabling robots to work in industrial environment to perform tasks like bin-picking or depalletizing. Even though there exist various pose estimation algorithms, they usually deal with common daily objects applied in lab environments. However, coping with real-world industrial objects is a much harder challenge for most pose estimation techniques due to the difficult material and structural properties of those objects. A comparative evaluation of pose estimation algorithms in regard to these object characteristics has yet to be done. This paper aims to provide a description and evaluation of selected state-of-the-art pose estimation techniques to investigate their object-related performance in terms of time and accuracy. The evaluation shows that there is indeed not a general algorithm which solves the task for all different objects, but it outlines the issues that real-world application have to deal with and what the strengths and weaknesses of the different pose estimation approaches are.

B. Großmann and M. Siam—Contributed equally to this work.

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Acknowledgment

Special thanks to Jörg Stückler and Bertram Drost for providing us with the necessary information to evaluate their developed algorithms. This work has partly been funded by the European Commission through grant agreement number 610917 (STAMINA).

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Authors and Affiliations

  1. Robotics, Vision and Machine Intelligence Lab, Department of Mechanical and Manufacturing Engineering, Aalborg University Copenhagen, Copenhagen, Denmark

    Bjarne Großmann, Mennatullah Siam & Volker Krüger

Authors
  1. Bjarne Großmann
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  2. Mennatullah Siam
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  3. Volker Krüger
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Corresponding author

Correspondence to Bjarne Großmann .

Editor information

Editors and Affiliations

  1. Aalborg University, Copenhagen, Denmark

    Lazaros Nalpantidis

  2. Aalborg University, Copenhagen, Denmark

    Volker Krüger

  3. Royal Institute of Technology - KTH, Stockholm, Sweden

    Jan-Olof Eklundh

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Gasteratos

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Großmann, B., Siam, M., Krüger, V. (2015). Comparative Evaluation of 3D Pose Estimation of Industrial Objects in RGB Pointclouds. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-20904-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20903-6

  • Online ISBN: 978-3-319-20904-3

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