Open AccessRoad Tunnel Detection Robot and Method Based on Laser Point Cloud
Author(s) -
Wenfeng Li,
Yulei Liu,
Ke Li,
Yong Peng,
Hu Ding,
Qiu-zhuo Liu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/741/1/012050
Subject(s) - point cloud , road surface , spall , computer vision , robustness (evolution) , computer science , robot , laser , laser scanning , artificial intelligence , point (geometry) , simulation , engineering , structural engineering , optics , mathematics , physics , biochemistry , chemistry , civil engineering , geometry , gene
The maintenance of road tunnels is more important, and there are problems such as water seepage, surface cracking and falling off. If it cannot be detected and handled in time, it will pose a major threat to the driving safety of road vehicles. Therefore, this paper proposes a road tunnel defect detection scheme based on laser point cloud. Firstly, a robot for road detection is developed. Secondly, a road defect detection method based on laser point cloud is developed. Laser SLAM technology is used to reconstruct dense point clouds in road tunnel scenes. Finally, through the automatic detection of the three-dimensional reconstruction scene of the tunnel, the defects such as cracks and spalling of the road tunnel are automatically identified. Compared with the visual detection scheme, this method does not depend on the problem of ambient light and has better robustness and practicability.