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

Document Type : Research Article


1 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Zachry Department of Civil Engineering, Texas A&M University, Texas, USA

3 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

4 Lyles School of Civil Engineering, Purdue University, West Lafayette, USA


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.


Main Subjects

[1] F.M. Nejad, H. Zakeri, A comparison of multi-resolution methods for detection and isolation of pavement distress, Expert Systems with Applications, 38(3) (2011) 2857-2872.
[2] H. Gonzalez-Jorge, B. Riveiro, E. Vazquez-Fernandez, J. Martínez-Sánchez, P. Arias, Metrological evaluation of microsoft kinect and asus xtion sensors, Measurement, 46(6) (2013) 1800-1806.
[3] A. Corti, S. Giancola, G. Mainetti, R. Sala, A metrological characterization of the Kinect V2 time-of-flight camera, Robotics and Autonomous Systems, 75 (2016) 584-594.
[4] H. Gonzalez-Jorge, P. Rodríguez-Gonzálvez, J. Martínez-Sánchez, D. González-Aguilera, P. Arias, M. Gesto, L. Díaz-Vilariño, Metrological comparison between Kinect I and Kinect II sensors, Measurement, 70 (2015) 21-26.
[5] A.M. Pinto, P. Costa, A.P. Moreira, L.F. Rocha, G. Veiga, E. Moreira, Evaluation of depth sensors for robotic applications, in: 2015 IEEE International Conference on Autonomous Robot Systems and Competitions, IEEE, 2015, pp. 139-143.
[6] E. Lachat, H. Macher, M. Mittet, T. Landes, P. Grussenmeyer, First experiences with Kinect v2 sensor for close range 3D modelling, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5) (2015) 93.
[7] T. Butkiewicz, Low-cost coastal mapping using Kinect v2 time-of-flight cameras, in: 2014 Oceans-St. John's, IEEE, 2014, pp. 1-9.
[8] K.H. McGhee, Automated pavement distress collection techniques, Transportation Research Board, 2004.
[9] R.A. El-laithy, J. Huang, M. Yeh, Study on the use of Microsoft Kinect for robotics applications, in: Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium, IEEE, 2012, pp. 1280-1288.
[10] P. Fankhauser, M. Bloesch, D. Rodriguez, R. Kaestner, M. Hutter, R. Siegwart, Kinect v2 for mobile robot navigation: Evaluation and modeling, in: 2015 International Conference on Advanced Robotics (ICAR), IEEE, 2015, pp. 388-394.
[11] S. Zennaro, Evaluation of Microsoft Kinect 360 and Microsoft Kinect One for robotics and computer vision applications, (2014).
[12] A. Mahmoudzadeh, S.F. Yeganeh, A. Golroo, Kinect, a novel cutting edge tool in pavement data collection, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1) (2015) 425.
[13] I. Moazzam, K. Kamal, S. Mathavan, S. Usman, M. Rahman, Metrology and visualization of potholes using the microsoft kinect sensor, in: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 1284-1291.
[14] K. Kamal, S. Mathavan, T. Zafar, I. Moazzam, A. Ali, S.U. Ahmad, M. Rahman, Performance assessment of Kinect as a sensor for pothole imaging and metrology, International Journal of Pavement Engineering, 19(7) (2018) 565-576.
[15] D. Joubert, A. Tyatyantsi, J. Mphahlehle, V. Manchidi, Pothole tagging system, (2011).
[16] T. Kim, S.-K. Ryu, Review and analysis of pothole detection methods, Journal of Emerging Trends in Computing and Information Sciences, 5(8) (2014) 603-608.
[17] C. Koch, G.M. Jog, I. Brilakis, Automated pothole distress assessment using asphalt pavement video data, Journal of Computing in Civil Engineering, 27(4) (2012) 370-378.
[18] M.R. Jahanshahi, F. Jazizadeh, S.F. Masri, B. Becerik-Gerber, Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor, Journal of Computing in Civil Engineering, 27(6) (2012) 743-754.
[19] S. Xie, 3D pavement surface reconstruction and cracking recognition using Kinect-based solution, Albuquerque, NM: Univ. of New Mexico Albuquerque, (2015).
[20] Y.L. Chen, M.R. Jahanshahi, P. Manjunatha, W. Gan, M. Abdelbarr, S.F. Masri, B. Becerik-Gerber, J.P. Caffrey, Inexpensive multimodal sensor fusion system for autonomous data acquisition of road surface conditions, IEEE Sensors Journal, 16(21) (2016) 7731-7743.
[21] G.H. Golub, C. Reinsch, Singular value decomposition and least squares solutions, in: Linear Algebra, Springer, 1971, pp. 134-151.
[22] K. Baker, Singular value decomposition tutorial, The Ohio State University, 24 (2005).
[23] J.A. Suykens, SVD revisited: A new variational principle, compatible feature maps and nonlinear extensions, Applied and Computational Harmonic Analysis, 40(3) (2016) 600-609.
[24] G.H. Golub, C.F. Van Loan, Matrix computations, JHU press, 2012.
[25] G.W. Stewart, On the early history of the singular value decomposition, SIAM review, 35(4) (1993) 551-566.
[26] C. Kim, S. Yun, S.-W. Jung, C.S. Won, Color and depth image correspondence for Kinect v2, in: Advanced Multimedia and Ubiquitous Engineering, Springer, 2015, pp. 111-116.
[27] M.R. Jahanshahi, Vision-based studies for structural health monitoring and condition assessment, University of Southern California, 2011.
[28] M. Brown, D.G. Lowe, Recognising Panoramas, in: ICCV, 2003, pp. 1218.
[29] H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features (SURF) Comput. Vis. Image Underst, in, New York, NY, USA: Elsevier Science Inc.,-3, 2008.
[30] P.H. Torr, A. Zisserman, MLESAC: A new robust estimator with application to estimating image geometry, Computer vision and image understanding, 78(1) (2000) 138-156.
[31] M. Brown, D.G. Lowe, Automatic panoramic image stitching using invariant features, International journal of computer vision, 74(1) (2007) 59-73.
[32] A.E.E. M, Standard Test Method for Measuring Rut-Depth of Pavement Surfaces Using a Straightedge, in, ASTM Standards, ASTM International USA, 2005.