TY - GEN
T1 - Road Environment Perception for Unmanned Motion Platform Based on Binocular Vision
AU - Liu, Xu
AU - Wang, Junzheng
AU - Li, Jiehao
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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
UR - http://www.scopus.com/inward/record.url?scp=85135831178&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-13844-7_19
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)
SP - 188
EP - 199
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 -