Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

Abstract

Pavement subsidence detection based on point cloud data acquired by mobile measurement systems is very challenging. First, the uncertainty and disorderly nature of object points data results in difficulties in point cloud comparison. Second, acquiring data with kinematic laser scanners introduces errors into systems during data acquisition, resulting in a reduction in data accuracy. Third, the high-precision measurement standard of pavement subsidence raises requirements for data processing. In this article, a data processing method is proposed to detect the subcentimeter-level subsidence of urban pavements using point cloud data comparisons in multiple time phases. The method mainly includes the following steps: First, the original data preprocessing is conducted, which includes point cloud matching and pavement point segmentation. Second, the interpolation of the pavement points into a regular grid is performed to solve the problem of point cloud comparison. Third, according to the high density of the pavement points and the performance of the pavement in the rough point cloud, using a Gaussian kernel convolution to smooth the pavement point cloud data, we aim to reduce the error in comparison. Finally, we determine the subsidence area by calculating the height difference and compare it with the threshold value. The experimental results show that the smoothing process can substantially improve the accuracy of the point cloud comparison results, effectively reducing the false detection rate and showing that subcentimeter-level pavement subsidence can be effectively detected.