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3D-Computer Vision for Automation of Logistic Processes

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Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

The availability of low-cost range sensors has led to several innovative implementations and solutions in various application fields like object recognition and localization, scene understanding, human-robot interaction or measurement of objects. The transfer of the corresponding methods and techniques to logistic processes needs the consideration of specific requirements. A logistic application field that requires robust and reliable 3D vision systems is automated handling of universal logistic goods for (de-)palletizing or unloading of standard containers in the field of sea and air cargo. This paper presents a 3D-computer vision system for recognizing and localizing different shaped logistic goods for automated handling by robotic systems. The objective is to distinguish between different types of goods like boxes, barrels or sacks due to their geometric shape in point cloud data. The system is evaluated with sensor data from a low-cost range sensor and ideal simulated data representing different shaped logistic goods as well.

Keywords

  • Automated logistics
  • 3D-computer vision
  • Object recognition
  • Machine learning

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Acknowledgments

This research is funded by the German Research Foundation (DFG) as part of the EMOSES project (SCHO 540/18-1).

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Correspondence to Hendrik Thamer .

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Thamer, H., Weimer, D., Kost, H., Scholz-Reiter, B. (2014). 3D-Computer Vision for Automation of Logistic Processes. In: Clausen, U., ten Hompel, M., Meier, J. (eds) Efficiency and Innovation in Logistics. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-01378-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-01378-7_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-01378-7

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