Object Classification of Laser Scanner by Using Recurrent Neural Network

Minho Cho, Jhonghyun An, Wonje Jang, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538654576
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of

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


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