Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 7; p. 1655
• Main Authors: Mahmoudzadeh, Ahmadreza, Golroo, Amir, Jahanshahi, Mohammad R, Firoozi Yeganeh, Sayna
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 06-04-2019; MDPI AG
• Subjects: 3D pavement surface reconstructing; depth data; International Roughness Index (IRI); Microsoft Kinect One (V2), Microsoft Kinect; outdoor imaging; pavement condition assessment; pavement health monitoring; pavement roughness; RGB-D sensor; time of flight sensor; visual sensing; International Roughness Index (IRI); time of flight sensor; RGB-D sensor; 3D pavement surface reconstructing; Microsoft Kinect One (V2), Microsoft Kinect; visual sensing; depth data; pavement condition assessment; pavement roughness; pavement health monitoring; outdoor imaging
• ISSN: 1424-8220; 1424-8220
• 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.