Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision

Xu Liu; Junzheng Wang; Jiehao Li

doi:10.1007/978-3-031-13844-7_19

Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision

Xu Liu, Junzheng Wang^*, Jiehao Li

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

Abstract

In order to enable the unmanned motion platform to obtain real-time environmental semantic information and obstacle depth information, a real-time semantic segmentation and feature point matching based on binocular cameras are considered. This method firstly takes advantages of a real-time semantic segmentation network to obtain the road scene information and the region of obstacles on the road such as vehicles or pedestrians. Then, feature matching is performed on the region of interest (ROI) of left and right views. In the experiment part, firstly we conduct simulation verification on the KITTI dataset, and then we conduct binocular camera calibration, rectification, segmentation and stereo matching based on Oriented FAST and Rotated BRIEF (ORB) method on the actual system. The experiment results proves that the method is real-time and robust.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
Editors	Honghai Liu, Weihong Ren, Zhouping Yin, Lianqing Liu, Li Jiang, Guoying Gu, Xinyu Wu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	188-199
Number of pages	12
ISBN (Print)	9783031138430
DOIs	https://doi.org/10.1007/978-3-031-13844-7_19
Publication status	Published - 2022
Event	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 - Harbin, China Duration: 1 Aug 2022 → 3 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13455 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
Country/Territory	China
City	Harbin
Period	1/08/22 → 3/08/22

Keywords

Binocular vision
Road environment perception
Semantic segmentation
Stereo matching
Unmanned motion platform

Access to Document

10.1007/978-3-031-13844-7_19

Cite this

Liu, X., Wang, J., & Li, J. (2022). Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision. In H. Liu, W. Ren, Z. Yin, L. Liu, L. Jiang, G. Gu, & X. Wu (Eds.), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings (pp. 188-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-13844-7_19

Liu, Xu ; Wang, Junzheng ; Li, Jiehao. / Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision. Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. editor / Honghai Liu ; Weihong Ren ; Zhouping Yin ; Lianqing Liu ; Li Jiang ; Guoying Gu ; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 188-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision",

abstract = "In order to enable the unmanned motion platform to obtain real-time environmental semantic information and obstacle depth information, a real-time semantic segmentation and feature point matching based on binocular cameras are considered. This method firstly takes advantages of a real-time semantic segmentation network to obtain the road scene information and the region of obstacles on the road such as vehicles or pedestrians. Then, feature matching is performed on the region of interest (ROI) of left and right views. In the experiment part, firstly we conduct simulation verification on the KITTI dataset, and then we conduct binocular camera calibration, rectification, segmentation and stereo matching based on Oriented FAST and Rotated BRIEF (ORB) method on the actual system. The experiment results proves that the method is real-time and robust.",

keywords = "Binocular vision, Road environment perception, Semantic segmentation, Stereo matching, Unmanned motion platform",

author = "Xu Liu and Junzheng Wang and Jiehao Li",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",

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doi = "10.1007/978-3-031-13844-7_19",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",

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Liu, X, Wang, J & Li, J 2022, Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision. in H Liu, W Ren, Z Yin, L Liu, L Jiang, G Gu & X Wu (eds), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13455 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 188-199, 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, Harbin, China, 1/08/22. https://doi.org/10.1007/978-3-031-13844-7_19

Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision. / Liu, Xu; Wang, Junzheng; Li, Jiehao.
Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. ed. / Honghai Liu; Weihong Ren; Zhouping Yin; Lianqing Liu; Li Jiang; Guoying Gu; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. p. 188-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI).

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

TY - GEN

T1 - Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision

AU - Liu, Xu

AU - Wang, Junzheng

AU - Li, Jiehao

PY - 2022

Y1 - 2022

N2 - In order to enable the unmanned motion platform to obtain real-time environmental semantic information and obstacle depth information, a real-time semantic segmentation and feature point matching based on binocular cameras are considered. This method firstly takes advantages of a real-time semantic segmentation network to obtain the road scene information and the region of obstacles on the road such as vehicles or pedestrians. Then, feature matching is performed on the region of interest (ROI) of left and right views. In the experiment part, firstly we conduct simulation verification on the KITTI dataset, and then we conduct binocular camera calibration, rectification, segmentation and stereo matching based on Oriented FAST and Rotated BRIEF (ORB) method on the actual system. The experiment results proves that the method is real-time and robust.

AB - In order to enable the unmanned motion platform to obtain real-time environmental semantic information and obstacle depth information, a real-time semantic segmentation and feature point matching based on binocular cameras are considered. This method firstly takes advantages of a real-time semantic segmentation network to obtain the road scene information and the region of obstacles on the road such as vehicles or pedestrians. Then, feature matching is performed on the region of interest (ROI) of left and right views. In the experiment part, firstly we conduct simulation verification on the KITTI dataset, and then we conduct binocular camera calibration, rectification, segmentation and stereo matching based on Oriented FAST and Rotated BRIEF (ORB) method on the actual system. The experiment results proves that the method is real-time and robust.

KW - Binocular vision

KW - Road environment perception

KW - Semantic segmentation

KW - Stereo matching

KW - Unmanned motion platform

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DO - 10.1007/978-3-031-13844-7_19

M3 - Conference contribution

AN - SCOPUS:85135831178

SN - 9783031138430

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

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BT - Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings

A2 - Liu, Honghai

A2 - Ren, Weihong

A2 - Yin, Zhouping

A2 - Liu, Lianqing

A2 - Jiang, Li

A2 - Gu, Guoying

A2 - Wu, Xinyu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022

Y2 - 1 August 2022 through 3 August 2022

ER -

Liu X, Wang J, Li J. Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision. In Liu H, Ren W, Yin Z, Liu L, Jiang L, Gu G, Wu X, editors, Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 188-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-13844-7_19