Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning

Marian Himstedt; Erik Maehle

Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning

Marian Himstedt, Erik Maehle

Institut für Technische Informatik

Institute of Computer Engineering

3 Zitate (Scopus)

Abstract

This paper focuses on the classification of obstacles that are widely present in warehouse environments using an RGBD camera. Our approach applies depth segmentation to detect obstacles which are classified using a Convolutional Neural Network and a Support Vector Machine. Our system is evaluated on real-world data captured from an automated reach truck in a warehouse environment.

Originalsprache	Englisch
Titel	Proceedings of ISR 2016: 47st International Symposium on Robotics
Redakteure/-innen	Marian Himstedt, Erik Maehle
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	01.01.2016
ISBN (Print)	978-3-8007-4231-8
Publikationsstatus	Veröffentlicht - 01.01.2016
Veranstaltung	Proceedings of ISR 2016: 47st International Symposium on Robotics - München, Deutschland Dauer: 21.06.2016 → 22.06.2016 https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7558447

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 9 – Industrie, Innovation und Infrastruktur

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@inproceedings{8e3e90f277ae4b679bf5859cb54418c3,

title = "Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning",

abstract = "This paper focuses on the classification of obstacles that are widely present in warehouse environments using an RGBD camera. Our approach applies depth segmentation to detect obstacles which are classified using a Convolutional Neural Network and a Support Vector Machine. Our system is evaluated on real-world data captured from an automated reach truck in a warehouse environment.",

author = "Marian Himstedt and Erik Maehle",

year = "2016",

month = jan,

day = "1",

language = "English",

isbn = "978-3-8007-4231-8",

editor = "Marian Himstedt and Erik Maehle",

booktitle = "Proceedings of ISR 2016: 47st International Symposium on Robotics",

publisher = "IEEE",

note = "Proceedings of ISR 2016: 47st International Symposium on Robotics ; Conference date: 21-06-2016 Through 22-06-2016",

url = "https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7558447",

}

Himstedt, M & Maehle, E 2016, Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning. in M Himstedt & E Maehle (Hrsg.), Proceedings of ISR 2016: 47st International Symposium on Robotics. IEEE, Proceedings of ISR 2016: 47st International Symposium on Robotics, München, Deutschland, 21.06.16. <https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7558447>

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AU - Himstedt, Marian

AU - Maehle, Erik

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N2 - This paper focuses on the classification of obstacles that are widely present in warehouse environments using an RGBD camera. Our approach applies depth segmentation to detect obstacles which are classified using a Convolutional Neural Network and a Support Vector Machine. Our system is evaluated on real-world data captured from an automated reach truck in a warehouse environment.

AB - This paper focuses on the classification of obstacles that are widely present in warehouse environments using an RGBD camera. Our approach applies depth segmentation to detect obstacles which are classified using a Convolutional Neural Network and a Support Vector Machine. Our system is evaluated on real-world data captured from an automated reach truck in a warehouse environment.

M3 - Conference contribution

SN - 978-3-8007-4231-8

BT - Proceedings of ISR 2016: 47st International Symposium on Robotics

A2 - Himstedt, Marian

A2 - Maehle, Erik

PB - IEEE

T2 - Proceedings of ISR 2016: 47st International Symposium on Robotics

Y2 - 21 June 2016 through 22 June 2016

ER -

Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning

Abstract

UN SDGs

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