Bin picking is a challenging problem common to many industries, whose automation will lead to great economic benefits. This paper presents a method for estimating the pose of a set of randomly arranged bent tubes, highly subject to occlusions and entanglement. The approach involves using a depth sensor to obtain a point cloud of the bin. The algorithm begins by filtering the point cloud to remove noise and segmenting it using the surface normals. Tube sections are then modeled as cylinders that are fitted into each segment using RANSAC. Finally, the sections are combined into complete tubes by adopting a greedy heuristic based on the distance between their endpoints. Experimental results with a dataset created with a Zivid sensor show that this method is able to provide estimates with high accuracy for bins with up to ten tubes. Therefore, this solution has the potential of being integrated into fully automated bin picking systems.


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    Titel :

    Perception of Entangled Tubes for Automated Bin Picking


    Weitere Titelangaben:

    Advs in Intelligent Syst., Computing


    Beteiligte:
    Silva, Manuel F. (Herausgeber:in) / Luís Lima, José (Herausgeber:in) / Reis, Luís Paulo (Herausgeber:in) / Sanfeliu, Alberto (Herausgeber:in) / Tardioli, Danilo (Herausgeber:in) / Leão, Gonçalo (Autor:in) / Costa, Carlos M. (Autor:in) / Sousa, Armando (Autor:in) / Veiga, Germano (Autor:in)

    Kongress:

    Iberian Robotics conference ; 2019 ; Porto, Portugal November 20, 2019 - November 22, 2019



    Erscheinungsdatum :

    20.11.2019


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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