We present a perception system for mobile manipulation tasks. The primary design goal of the proposed system is to minimize human interaction during system setup which is achieved by several means, such as automatic training data generation, the use of simulated training data, and 3D model based geometric matching. We employ a state-of-the art deep-learning based bounding box detector for rough localization of objects and a Point Pair Feature based matching algorithm for 6DoF pose estimation. The proposed approach shows promising results on our recently published dataset for industrial object detection and pose estimation. Furthermore, the system’s performance during four days of live operation at the Automatica 2018 trade fair is analyzed and failure cases are presented and discussed.


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    Titel :

    6DoF Pose Estimation for Industrial Manipulation Based on Synthetic Data


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Xiao, Jing (Herausgeber:in) / Kröger, Torsten (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Brucker, Manuel (Autor:in) / Durner, Maximilian (Autor:in) / Márton, Zoltán-Csaba (Autor:in) / Bálint-Benczédi, Ferenc (Autor:in) / Sundermeyer, Martin (Autor:in) / Triebel, Rudolph (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Erscheinungsdatum :

    2020-01-23


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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