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
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aldoma, A., Tombari, F., Di Stefano, L., Vincze, M.: A global hypotheses verification method for 3D object recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C., eds.: Computer Vision ECCV 2012. Volume 7574 of Lecture Notes in Computer Science, pp. 511–524, Springer Berlin Heidelberg (2012)
Scholz-Reiter, B., Echelmeyer, W., Wellbrock, E.: Development of a robot-based system for automated unloading of variable packages out of transport units and containers. In: IEEE International Conference on Automation and Logistics. 2766–2770 (2008)
Echelmeyer, W., Kirchheim, A., Lilienthal, A.J., Akbiyik, H., Bonini, M.: Performance indicators for robotics systems in logistics applications. In: IEEE/RSJ IROS Workshop on Metrics and Methodologies for Autonomous Robot Teams in Logistics (2011)
Aldoma, A., Tombari, F., Rusu, R. B., Vincze, M.: Our-cvfh oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6D of pose estimation. In: Pinz, A., Pock, T., Bischof, H., Leberl, F., eds.: Pattern Recognition. Volume 7476 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 113–122 (2012)
Rusu, R. B., Bradski, G. R., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the viewpoint feature histogram. In: Proceedings of the 23rd IEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2155–2162, Taipei, Taiwan (2010)
Aldoma, A., Vincze, M., Blodow, N., Gossow, D., Gedikli, S., Rusu, R. B., Bradski, G. R.: CAD-model recognition and 6DOF pose estimation using 3D cues. In: IEEE International Conference on Computer Vision Workshops, 585–592, Barcelona, Spain, (2011)
Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3D object classification. In: IEEE International Conference on Robotics and Biomimetics (ROBIO). 2987–2992 (2011)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)
Rusu, R. B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3D registration. In: IEEE International Conference on Robotics and Automation, 3212–3217, Kobe, Japan (2009)
Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Proceedings of the 11th European Conference on Computer Vision: Part III. 356–369 (2010)
Acknowledgments
This research is funded by the German Research Foundation (DFG) as part of the EMOSES project (SCHO 540/18-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-01378-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01377-0
Online ISBN: 978-3-319-01378-7
eBook Packages: EngineeringEngineering (R0)