Three-dimensional pose estimation of deformable linear object tips based on a low-cost, two-dimensional sensor setup and AI-based evaluation

Fröhlig S, von Fabris auf Mayerhofen M, Meiners M, Franke J (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Elsevier B.V.

Book Volume: 107

Pages Range: 499-504

Conference Proceedings Title: Procedia CIRP

Event location: Lugano

DOI: 10.1016/j.procir.2022.05.015

Abstract

Handling and processing rigid objects is a common task for industrial robots. However, regarding flexible objects, this task is considerably more complex because gravity and other external forces influence the shape of the objects. Moreover, especially deformable linear objects like cables can show a large deformation. Therefore, regarding the automation of handling and assembly processes with robots, it is crucial to determine the current state of the shape of the cables. Furthermore, to significantly reduce the engineering cost and time, we introduce a method for utilizing synthetic data and thus reduce data acquisition and preparation efforts. The basic approach of detection is separated into four steps. Firstly, object detection identifies and classifies cable tips from two two-dimensional pictures taken from different perspectives. Secondly, keypoint detection identifies the pixel defining the start and endpoint of the identified object class. Thirdly, a triangulation merges the evaluations of the single images with the geometrical arrangement of the setup. Finally, a correction calculation compensates systematic errors caused by the inexactness of the physical camera setup based on an initially performed calibration algorithm performed for each new physical setup. The results presented in this paper prove the feasibility and advantage of the approach and show the impact of different characteristics on the accuracy of the three-dimensional pose estimation.

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How to cite

APA:

Fröhlig, S., von Fabris auf Mayerhofen, M., Meiners, M., & Franke, J. (2022). Three-dimensional pose estimation of deformable linear object tips based on a low-cost, two-dimensional sensor setup and AI-based evaluation. In Anna Valente, Emanuele Carpanzano, Claudio Boer (Eds.), Procedia CIRP (pp. 499-504). Lugano, CH: Elsevier B.V..

MLA:

Fröhlig, Simon, et al. "Three-dimensional pose estimation of deformable linear object tips based on a low-cost, two-dimensional sensor setup and AI-based evaluation." Proceedings of the 55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022, Lugano Ed. Anna Valente, Emanuele Carpanzano, Claudio Boer, Elsevier B.V., 2022. 499-504.

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