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Data-Driven Object Pose Estimation in a Practical Bin-Picking Application

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00352836" target="_blank" >RIV/68407700:21230/21:00352836 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/21:00352836

  • Result on the web

    <a href="https://doi.org/10.3390/s21186093" target="_blank" >https://doi.org/10.3390/s21186093</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s21186093" target="_blank" >10.3390/s21186093</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Data-Driven Object Pose Estimation in a Practical Bin-Picking Application

  • Original language description

    This paper addresses the problem of pose estimation from 2D images for textureless industrial metallic parts for a semistructured bin-picking task. The appearance of metallic reflective parts is highly dependent on the camera viewing direction, as well as the distribution of light on the object, making conventional vision-based methods unsuitable for the task. We propose a solution using direct light at a fixed position to the camera, mounted directly on the robot's gripper, that allows us to take advantage of the reflective properties of the manipulated object. We propose a data-driven approach based on convolutional neural networks (CNN), without the need for a hard-coded geometry of the manipulated object. The solution was modified for an industrial application and extensively tested in a real factory. Our solution uses a cheap 2D camera and allows for a semi-automatic data-gathering process on-site.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Sensors

  • ISSN

    1424-8220

  • e-ISSN

    1424-8220

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    18

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    23

  • Pages from-to

  • UT code for WoS article

    000701557400001

  • EID of the result in the Scopus database

    2-s2.0-85114601266