Object Classification of Laser Scanner by Using Recurrent Neural Network

Minho Cho; Jhonghyun An; Wonje Jang; Euntai Kim

doi:10.1109/TENCON.2018.8650478

Object Classification of Laser Scanner by Using Recurrent Neural Network

Minho Cho, Jhonghyun An, Wonje Jang, Euntai Kim

Department of Electrical and Electronic Engineering

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

Abstract

These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.

Original language	English
Title of host publication	Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1440-1444
Number of pages	5
ISBN (Electronic)	9781538654576
DOIs	https://doi.org/10.1109/TENCON.2018.8650478
Publication status	Published - 2019 Feb 22
Event	2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of Duration: 2018 Oct 28 → 2018 Oct 31

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2018-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2018 IEEE Region 10 Conference, TENCON 2018
Country/Territory	Korea, Republic of
City	Jeju
Period	18/10/28 → 18/10/31

Bibliographical note

Funding Information:
VI. ACKNOWLEDGMENT This work was supported by the Industrial Convergence Core Technology Development Program(No. 10063172, Development of robot intelligence technology for mobility with learning capability toward robust and seamless indoor and outdoor autonomous navigation) funded by the Ministry of Trade, industry & Energy (MOTIE), Korea.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2018.8650478

Cite this

Cho, M., An, J., Jang, W., & Kim, E. (2019). Object Classification of Laser Scanner by Using Recurrent Neural Network. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1440-1444). Article 8650478 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650478

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title = "Object Classification of Laser Scanner by Using Recurrent Neural Network",

abstract = "These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.",

author = "Minho Cho and Jhonghyun An and Wonje Jang and Euntai Kim",

note = "Funding Information: VI. ACKNOWLEDGMENT This work was supported by the Industrial Convergence Core Technology Development Program(No. 10063172, Development of robot intelligence technology for mobility with learning capability toward robust and seamless indoor and outdoor autonomous navigation) funded by the Ministry of Trade, industry & Energy (MOTIE), Korea. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Region 10 Conference, TENCON 2018 ; Conference date: 28-10-2018 Through 31-10-2018",

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doi = "10.1109/TENCON.2018.8650478",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

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Cho, M, An, J, Jang, W & Kim, E 2019, Object Classification of Laser Scanner by Using Recurrent Neural Network. in Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference., 8650478, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-October, Institute of Electrical and Electronics Engineers Inc., pp. 1440-1444, 2018 IEEE Region 10 Conference, TENCON 2018, Jeju, Korea, Republic of, 18/10/28. https://doi.org/10.1109/TENCON.2018.8650478

Object Classification of Laser Scanner by Using Recurrent Neural Network. / Cho, Minho; An, Jhonghyun; Jang, Wonje et al.
Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1440-1444 8650478 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October).

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

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N1 - Funding Information: VI. ACKNOWLEDGMENT This work was supported by the Industrial Convergence Core Technology Development Program(No. 10063172, Development of robot intelligence technology for mobility with learning capability toward robust and seamless indoor and outdoor autonomous navigation) funded by the Ministry of Trade, industry & Energy (MOTIE), Korea. Publisher Copyright: © 2018 IEEE.

PY - 2019/2/22

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N2 - These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.

AB - These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.

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Object Classification of Laser Scanner by Using Recurrent Neural Network

Abstract

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Bibliographical note

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