Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843-3136, USA.
Department of Civil and Environment Engineering, Amirkabir University of Technology, Tehran, Iran, 424 Hafez Ave, Tehran 15875-4413, Iran.
Sensors (Basel). 2019 Apr 6;19(7):1655. doi: 10.3390/s19071655.
Measuring pavement roughness and detecting pavement surface defects are two of the most important tasks in pavement management. While existing pavement roughness measurement approaches are expensive, the primary aim of this paper is to use a cost-effective and sufficiently accurate RGB-D sensor to estimate the pavement roughness in the outdoor environment. An algorithm is proposed to process the RGB-D data and autonomously quantify the road roughness. To this end, the RGB-D sensor is calibrated and primary data for estimating the pavement roughness are collected. The collected depth frames and RGB images are registered to create the 3D road surfaces. We found that there is a significant correlation between the estimated International Roughness Index (IRI) using the RGB-D sensor and the manual measured IRI using rod and level. By considering the Power Spectral Density (PSD) analysis and the repeatability of measurement, the results show that the proposed solution can accurately estimate the different pavement roughness.
测量路面粗糙度和检测路面表面缺陷是路面管理中最重要的两项任务。虽然现有的路面粗糙度测量方法成本高昂，但本文的主要目的是使用经济高效且足够精确的 RGB-D 传感器来估算户外环境中的路面粗糙度。本文提出了一种算法来处理 RGB-D 数据并自动量化道路粗糙度。为此，对 RGB-D 传感器进行了校准，并采集了用于估算路面粗糙度的原始数据。采集的深度帧和 RGB 图像被注册以创建 3D 道路表面。我们发现，使用 RGB-D 传感器估算的国际平整度指数（IRI）与使用杆和水准仪手动测量的 IRI 之间存在显著相关性。通过考虑功率谱密度（PSD）分析和测量的可重复性，结果表明，所提出的解决方案可以准确估计不同的路面粗糙度。
