The Relative Confusion Matrix, a Tool to Assess Classifiablility in Large Scale Picking Applications

Alexander Balasch, Maximilian Beinhofer, Gerald Zauner

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

For bin picking robots in real logistics installations, the certainty of picking the correct product out of a mixed-product bin is essential. This paper proposes an approach for the robot to efficiently decide whether it can robustly distinguish the product to pick from the others in the bin. If not, the pick has to be routed not to the robot workstation but to a manual picking station. For this, we introduce a modified version of the confusion matrix, which we call the relative confusion matrix. We show how this matrix can be used to make the required decision, taking into account that all other products in the warehouse can be logically ruled out as they are not contained in the bin. Considering only this subset of products would require a re-computation of the standard confusion matrix. With the relative confusion matrix, no such re-computation is needed, which makes our approach more efficient. We show the usefulness of our approach in extensive experiments with a real bin picking robot, on simulated data, and on a publicly available image dataset.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8390-8396
Number of pages7
ISBN (Electronic)9781728173955
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
CountryFrance
CityParis
Period31.05.202031.08.2020

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