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博士生宋红霞,张继贤的论文在REMOTE SENSING刊出
发布时间:2022-06-24 10:23:11     发布者:易真     浏览次数:

标题: Subsidence Detection for Urban Roads Using Mobile Laser Scanner Data

作者: Song, HX (Song, Hongxia); Zhang, JX (Zhang, Jixian); Zuo, JZ (Zuo, Jianzhang); Liang, XL (Liang, Xinlian); Han, WL (Han, Wenli); Ge, J (Ge, Juan)

来源出版物: REMOTE SENSING : 14 : 9 文献号: 2240 DOI: 10.3390/rs14092240 出版年: MAY 2022

摘要: 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.

作者关键词: mobile laser scanner; subsidence detection; point cloud comparison; Gaussian smoothing

地址: [Song, Hongxia; Zhang, Jixian] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

[Song, Hongxia; Zhang, Jixian; Han, Wenli; Ge, Juan] Natl Qual Inspect & Testing Ctr Surveying & Mappi, Beijing 100830, Peoples R China.

[Zuo, Jianzhang] Chinese Acad Surveying & Mapping, Beijing 100036, Peoples R China.

[Liang, Xinlian] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

通讯作者地址: Zhang, JX (通讯作者)Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

Zhang, JX (通讯作者)Natl Qual Inspect & Testing Ctr Surveying & Mappi, Beijing 100830, Peoples R China.

电子邮件地址: hongxiasong@whu.edu.cn; zhangjx@casm.ac.cn; zuojz@casm.ac.cn; xinlian.liang@whu.edu.cn; 13910993256@139.com; gejuan-2004@163.com

影响因子:4.848


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