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Processing of mobile laser scanning data for large‐scale deformation monitoring of anchored retaining structures along highways
Author(s) -
Kalenjuk Slaven,
Lienhart Werner,
Rebhan Matthias J.
Publication year - 2021
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12656
Subject(s) - redundancy (engineering) , point cloud , automation , exploit , computer science , computation , data redundancy , laser scanning , data processing , pipeline (software) , cloud computing , real time computing , data mining , engineering , computer vision , laser , algorithm , mechanical engineering , computer security , database , physics , optics , programming language , operating system
In times of steadily increasing traffic loads and extreme weather phenomena, the safe maintenance of infrastructure poses a difficult challenge to operators, especially when a vast number of aged structures exists and fundamental data is missing. This paper addresses the demand for cost‐efficient deformation monitoring of anchored retaining structures along public roads. The principal idea is to process laser scans of a motor‐vehicle‐based mobile mapping system with a high degree of automation. Starting with scene interpretation, our processing pipeline extracts the retaining wall from the rest of the point cloud, segments the anchored elements, and computes their deformations. This method requires, however, correcting for positioning errors to obtain accurate results. We exploit the high data redundancy of road patches and line markings for alignment. Due to the high degree of automation, computations scale to large numbers of point clouds and run in a repeatable manner. Even when traveling along highways with up to 100 km/h, we achieve repeatable accuracies for tilting and lateral displacements that compare to traditional, labor‐intense surveying methods.