A RGBD-Based System for Real-Time Robotic Defects Detection on Sewer Networks

  • Luis MerinoEmail author
  • David Alejo
  • Simón Martinez-Rozas
  • Fernando Caballero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


In this paper we summarize the automatic defect inspection onboard the sewer inspection ground platform SIAR. We include a general overview of the software and hardware characteristics of our platform, making a special emphasis on the sensing devices and software systems that are used for defect inspection. The main detection algorithm makes use of the a priori knowledge of ideal sections of the sewers that can be found in the Geographic Information Systems (GIS), and uses a variant of the Iterative Closest Point (ICP) algorithm for finding structural and serviceability defects. Then, we describe the software modules that are in charge of storing the alerts found by the detection system and of displaying them to the operator. The whole system has been tested in two field scenarios on different locations of the real sewer network of Barcelona, Spain.


Sewer inspection Defect detection Field robotics 


  1. 1.
    Kuntze, H., Haffner, H.: Experiences with the development of a robot for smart multisensoric pipe inspection. In: IEEE International Conference on Robotics and Automation, vol. 2, pp. 1773–1778 (1998).
  2. 2.
    Kirkham, R., Kearney, P.D., Rogers, K.J., Mashford, J.: PIRAT - a system for quantitative sewer pipe assessment. Int. J. Robot. Res. 19(11), 1033–1053 (2000). Scholar
  3. 3.
    Moselhi, O., Shehab, T.: Automated detection of surface defects in water and sewer pipes. Autom. Constr. 8(5), 581–588 (1999). Scholar
  4. 4.
    Shehab, T., Moselhi, O.: Automated detection and classification of infiltration in sewer pipes. J. Infrastruct. Syst. 11(3), 165–171 (2005)CrossRefGoogle Scholar
  5. 5.
    Halfawy, M.R., Hengmeechai, J.: Integrated vision-based system for automated defect detection in sewer closed circuit television inspection videos. J. Comput. Civ. Eng. 29(1), 04014024 (2015). Scholar
  6. 6.
    Moradi, S. and Zayed, T.: Real-time defect detection in sewer closed circuit television inspection videos, pp. 295-307 (2017).
  7. 7.
    ECHORD++: Utility infrastructures and condition monitoring for sewer network. Robots for the inspection and the clearance of the sewer network in cities (2014).
  8. 8.
    Alejo, D., Caballero, F. and Merino, L.: RGBD-based robot localization in sewer networks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4070–4076, September 2017.
  9. 9.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992). Scholar
  10. 10.
    Kam, H., Lee, S.-H., Park, T., Kim, C.-H.: RViz: a toolkit for real domain data visualization. Telecommun. Syst. 60, 337–345 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Luis Merino
    • 1
    Email author
  • David Alejo
    • 1
  • Simón Martinez-Rozas
    • 1
  • Fernando Caballero
    • 1
  1. 1.Service Robotics LaboratoryUniversidad Pablo de OlavideSevilleSpain

Personalised recommendations