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KLT Bin Detection and Pose Estimation in an Industrial Environment. / Beloshapko, Aleksei; Knoll, Christian; Boughattas, Bilel; Korkhov, Vladimir.

Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. ed. / Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Chiara Garau; Ivan Blecic; David Taniar; Bernady O. Apduhan; Ana Maria A.C. Rocha; Eufemia Tarantino; Carmelo Maria Torre; Yeliz Karaca. Springer Nature, 2020. p. 105-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12254 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Beloshapko, A, Knoll, C, Boughattas, B & Korkhov, V 2020, KLT Bin Detection and Pose Estimation in an Industrial Environment. in O Gervasi, B Murgante, S Misra, C Garau, I Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino, CM Torre & Y Karaca (eds), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12254 LNCS, Springer Nature, pp. 105-118, 20th International Conference on Computational Science and Its Applications, ICCSA 2020, Cagliari, Italy, 1/07/20. https://doi.org/10.1007/978-3-030-58817-5_9

APA

Beloshapko, A., Knoll, C., Boughattas, B., & Korkhov, V. (2020). KLT Bin Detection and Pose Estimation in an Industrial Environment. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (Eds.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings (pp. 105-118). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12254 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-58817-5_9

Vancouver

Beloshapko A, Knoll C, Boughattas B, Korkhov V. KLT Bin Detection and Pose Estimation in an Industrial Environment. In Gervasi O, Murgante B, Misra S, Garau C, Blecic I, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, Karaca Y, editors, Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Springer Nature. 2020. p. 105-118. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-58817-5_9

Author

Beloshapko, Aleksei ; Knoll, Christian ; Boughattas, Bilel ; Korkhov, Vladimir. / KLT Bin Detection and Pose Estimation in an Industrial Environment. Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. editor / Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Chiara Garau ; Ivan Blecic ; David Taniar ; Bernady O. Apduhan ; Ana Maria A.C. Rocha ; Eufemia Tarantino ; Carmelo Maria Torre ; Yeliz Karaca. Springer Nature, 2020. pp. 105-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{24b9f149fe1044e0aee9b421bc10b245,
title = "KLT Bin Detection and Pose Estimation in an Industrial Environment",
abstract = "In order for Automated Guided Vehicles (AGV{\textquoteright}s) to handle KLT bins (Kleinladungstr{\"a}ger, Small Load Carrier) in a flexible way, a robust bin detection algorithm has to be developed. This paper presents a solution to the KLT bin detection and pose estimation task. The Mask R-CNN network is used to detect a KLT bin on color images, while a simple plane fitting approach is used to estimate its 5DoF position. This combination gives promising results in a typical use case scenario when the KLT bin is aligned with the camera view.",
keywords = "Industry 4.0, Instance segmentation, KLT bin picking, Object detection, Plane fitting, Pose estimation",
author = "Aleksei Beloshapko and Christian Knoll and Bilel Boughattas and Vladimir Korkhov",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 20th International Conference on Computational Science and Its Applications, ICCSA 2020 ; Conference date: 01-07-2020 Through 04-07-2020",
year = "2020",
doi = "10.1007/978-3-030-58817-5_9",
language = "English",
isbn = "9783030588168",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "105--118",
editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Blecic and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria A.C.} and Eufemia Tarantino and Torre, {Carmelo Maria} and Yeliz Karaca",
booktitle = "Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings",
address = "Germany",
url = "http://iccsa.org/",

}

RIS

TY - GEN

T1 - KLT Bin Detection and Pose Estimation in an Industrial Environment

AU - Beloshapko, Aleksei

AU - Knoll, Christian

AU - Boughattas, Bilel

AU - Korkhov, Vladimir

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - In order for Automated Guided Vehicles (AGV’s) to handle KLT bins (Kleinladungsträger, Small Load Carrier) in a flexible way, a robust bin detection algorithm has to be developed. This paper presents a solution to the KLT bin detection and pose estimation task. The Mask R-CNN network is used to detect a KLT bin on color images, while a simple plane fitting approach is used to estimate its 5DoF position. This combination gives promising results in a typical use case scenario when the KLT bin is aligned with the camera view.

AB - In order for Automated Guided Vehicles (AGV’s) to handle KLT bins (Kleinladungsträger, Small Load Carrier) in a flexible way, a robust bin detection algorithm has to be developed. This paper presents a solution to the KLT bin detection and pose estimation task. The Mask R-CNN network is used to detect a KLT bin on color images, while a simple plane fitting approach is used to estimate its 5DoF position. This combination gives promising results in a typical use case scenario when the KLT bin is aligned with the camera view.

KW - Industry 4.0

KW - Instance segmentation

KW - KLT bin picking

KW - Object detection

KW - Plane fitting

KW - Pose estimation

UR - http://www.scopus.com/inward/record.url?scp=85092609797&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/339668e0-c75d-359a-948f-3c924ef2e424/

U2 - 10.1007/978-3-030-58817-5_9

DO - 10.1007/978-3-030-58817-5_9

M3 - Conference contribution

AN - SCOPUS:85092609797

SN - 9783030588168

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 105

EP - 118

BT - Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Misra, Sanjay

A2 - Garau, Chiara

A2 - Blecic, Ivan

A2 - Taniar, David

A2 - Apduhan, Bernady O.

A2 - Rocha, Ana Maria A.C.

A2 - Tarantino, Eufemia

A2 - Torre, Carmelo Maria

A2 - Karaca, Yeliz

PB - Springer Nature

T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020

Y2 - 1 July 2020 through 4 July 2020

ER -

ID: 70614972