Journal
SENSORS
Volume 19, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/s19071655
Keywords
RGB-D sensor; depth data; Microsoft Kinect One (V2); Microsoft Kinect; visual sensing; time of flight sensor; 3D pavement surface reconstructing; outdoor imaging; pavement roughness; International Roughness Index (IRI); pavement condition assessment; pavement health monitoring
Funding
- Office of the Vice President for Research
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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.
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