Visual Data Simulation for Deep Learning in Robot Manipulation Tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332335" target="_blank" >RIV/68407700:21230/19:00332335 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/19:00332335

Result on the web

<a href="https://doi.org/10.1007/978-3-030-14984-0_29" target="_blank" >https://doi.org/10.1007/978-3-030-14984-0_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14984-0_29" target="_blank" >10.1007/978-3-030-14984-0_29</a>

Result language

angličtina

Original language name

Visual Data Simulation for Deep Learning in Robot Manipulation Tasks

Original language description

This paper introduces the usage of simulated images for training convolutional neural networks for object recognition and localization in the task of random bin picking. For machine learning applications, a limited amount of real world image data that can be captured and labeled for training and testing purposes is a big issue. In this paper, we focus on the use of realistic simulation of image data for training convolutional neural networks to be able to estimate the pose of an object. We can systematically generate varying camera viewpoint datasets with a various pose of an object and lighting conditions. After successful training and testing the neural network, we compare the performance of network trained on simulated images and images from a real camera capturing the physical object. The usage of the simulated data can speed up the complex and time-consuming task of gathering training data as well as increase robustness of object recognition by generating a bigger amount of data.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

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

Article name in the collection

Modelling and Simulation for Autonomous Systems (MESAS 2018)

ISBN

978-3-030-14983-3

ISSN

0302-9743

e-ISSN

—

Number of pages

10

Pages from-to

402-411

Publisher name

Springer International Publishing AG

Place of publication

Cham

Event location

Praha

Event date

Oct 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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