SIAR: A Ground Robot Solution for Semi-autonomous Inspection of Visitable Sewers

  • David Alejo
  • Gonzalo Mier
  • Carlos Marques
  • Fernando CaballeroEmail author
  • Luís Merino
  • Paulo Alvito
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 132)


The SIAR platform is a six-wheeled ground robot with differential kinematic configuration and automatic width adjustment developed for the ECHORD++ Challenge on Urban Robotics: “Robots For The Inspection And The Clearance Of The Sewer Network In Cities”. This challenge proposes the development of a wireless robotic platform for long range inspection of large city sewers, which are currently not addressed by commercial solutions. SIAR leverages RGBD data for affordable and high-resolution 3D perception of its surroundings. This information is internally used for robot localization and safe navigation. Moreover, this information is also employed in high-level functionalities such as automatic defect inspection, the detection of serviceability losses and the generation of global 3D reconstructions of the environment. This chapter describes the main software and hardware architecture of the system. It also details the advances made over state-of-the-art techniques in order to take into account the particularities of this environment, i.e., localization in a GPS-denied area, navigation or communications, to name a few. Finally, the chapter presents experimental results on real sewers of Barcelona to demonstrate the reliability and suitability of the proposed solution.


  1. 1.
    ECHORD++: Utility infrastructures and condition monitoring for sewer network. Robots for the inspection and the clearance of the sewer network in cities. (2014)
  2. 2.
    Mirats-Tur, J., Garthwaite, W.: Robotic devices for water main in-pipe inspection: a survey. J. Field Robot., 491–508 (2010)CrossRefGoogle Scholar
  3. 3.
    Walter, C., Saenz, J., Elkmann, N., Althoff, H., Kutzner, S., Stuerze, T.: Design considerations of robotic system for cleaning and inspection of large-diameter sewers. J. Field Robot. 29(1), 186–214CrossRefGoogle Scholar
  4. 4.
  5. 5.
  6. 6.
    Solo tracked robots from redzone.
  7. 7.
  8. 8.
    Purerobotics’ pipeline inspection.
  9. 9.
  10. 10.
    Tardioli, D.: A proof-of-concept application of multi-hop robot teleoperation with online map building. In: Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014), pp. 210–217. IEEE (2014)Google Scholar
  11. 11.
    Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) Workshop on Open Source Robotics. Kobe, Japan (2009)Google Scholar
  12. 12.
    Alejo, D., Caballero, F., Merino, L.: RGBD-based robot localization in sewer networks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4070–4076 (2017)Google Scholar
  13. 13.
    Perez-Grau, F.J., Fabresse, F.R., Caballero, F., Viguria, A., Ollero, A.: Long-term aerial robot localization based on visual odometry and radio-based ranging. In: Proceedings of the 2016 International Conference on Unmanned Aerial Systems. Arlintong, USA (2016)Google Scholar
  14. 14.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128(1–2), 99–141 (2001)CrossRefGoogle Scholar
  15. 15.
    Papadakis, P.: Terrain traversability analysis methods for unmanned ground vehicles: a survey. Eng. Appl. Artif. Intell. 26(4), 1373–1385 (2013)CrossRefGoogle Scholar
  16. 16.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)CrossRefGoogle Scholar
  17. 17.
    Krüsi, P., Furgale, P., Bosse, M., Siegwart, R.: Driving on point clouds: motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. J. Field Robot. 34(5), 940–984CrossRefGoogle Scholar
  18. 18.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013). Software available at
  19. 19.
    Grisetti, G., Kummerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based slam. IEEE Intell. Transp. Syst. Mag. 2(4), 31–43 (2010)CrossRefGoogle Scholar
  20. 20.
    Perez-Grau, F., Caballero, F., Merino, L., Viguria, A.: Multi-modal mapping and localization of unmanned aerial robots based on ultra-wideband and RGB-D sensing. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Vancouver, BC, Canada, September 24–28, pp. 3495–3502 (2017)Google Scholar
  21. 21.
    Pérez-Lara, J., Caballero, F., Merino, L.: Enhanced monte carlo localization with visual place recognition for robust robot localization. J. Intell. Robot. Syst. 80, 641–656 (2015)CrossRefGoogle Scholar
  22. 22.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • David Alejo
    • 1
  • Gonzalo Mier
    • 1
  • Carlos Marques
    • 2
  • Fernando Caballero
    • 1
    Email author
  • Luís Merino
    • 1
  • Paulo Alvito
    • 2
  1. 1.Service Robotics LaboratoryUniversidad Pablo de OlavideSevilleSpain
  2. 2.IDMindLisbonPortugal

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