Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces

Abstract

In many construction and infrastructure management projects, it is important to ensure the flatness of concrete surfaces. Inspectors assess the quality of flat surface construction by checking whether a surface deviates from perfectly flat by more than a specified tolerance. Current flatness assessment methods, such as using a straightedge or shape profiler, are limited in the speed or density of their measurements. Laser scanners are general-purpose instruments for densely and accurately measuring three-dimensional shapes. In this paper, we show how laser scanners can be effectively used to assess surface flatness. Specifically, we formalize, implement, and validate three algorithms for processing laser-scanned data to detect surface flatness deviations. Since different scanners and algorithms can perform differently, we define an evaluation framework for objectively evaluating the performance of different algorithms and scanners. Using this framework, we analyze and compare the performance of the three algorithms using data from three laser scanners. The results show that it is possible to detect surface flatness defects as small as 3 cm across and 1 mm thick from a distance of 20 m.