Road Tunnel Detection Robot and Method Based on Laser Point Cloud

Abstract

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.

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

“…The method is reported to be flexible when combined with other anomaly prediction models. The U.S. Department of Transportation (USDOT) devised a convention to rank road surface distress [ 59 ], as shown in Equation (3). The pavement condition index (PCI) is generated using a weighted sum of surface condition rating (SCR) and roughness condition index (RCI).…”