Pavement Distress Data Collection and 3D Pavement Surface Reconstruction Using Kinect Sensor

The most important part in pavement management systems is data collection. The modern technologies which are used for this purpose, such as point-based lasers and laser scanners, are too expensive to purchase, operate, and maintain. Thus, it is rarely feasible for city officials in developing countries to conduct data collection using these devices. This paper aims to introduce a cost-effective technology which can be used for pavement distress data collection and 3D pavement surface reconstruction. The applied technology in this research is the Kinect sensor which is not only cost-effective but also sufficiently precise. The Kinect sensor can register both depth and color images simultaneously. An apparatus is designed and developed to hold an array of Kinect sensors. The cameras are calibrated and the slopes of collected images from surfaces are corrected via the Singular Value Decomposition (SVD) algorithm. Then, a procedure is proposed for stitching the RGB_D (Red Green Blue–Depth) images using SURF (Speeded-up Robust Features) and MSAC (M-estimator SAmple Consensus) algorithms in order to create a 3D-structure of the pavement surface. Finally, transverse profiles are extracted and some field experiments are conducted to evaluate the validity of proposed approach for detecting pavement surface defects.