Abstract：The road surface settlement is the key to influence the safety of driving during the operation and maintenance of expressways. Conventional inspections are mostly based on leveling, which have the disadvantages of heavy workload, long cycle and easy to be affected by external conditions. An algorithm and analysis method for automatically identifying pavement subsidence by ground 3D laser scanning point cloud was proposed. In the proposed method, the point cloud was registered firstly based on the combination of geometric features and iterative closest point algorithm. After the registration data was classified and identified by point cloud, the road feature line was extracted based on the sliding window method and the pavement model of the expressway was established. By analyzing the proposed model, the indexes of subsidence area, location and depth were obtained. Finally, a highway at the operation and maintenance stage was adopted to verify the feasibility and accuracy of the proposed algorithm. Through the algorithm processing and the actual pavement model analysis, it is found that there is a settlement area with a depth of 40 cm at 45 m away from the starting point of the experimental section of the expressway. The analysis results show that the proposed algorithm of laser point cloud data processing and pavement model analysis can efficiently and accurately analyze the settlement of expressway pavement. The successful application of the proposed method is of great significance for the deep application and effective promotion of 3D laser scanning technology in the traffic field.
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2020, Vol. 39 
