Skip to main content

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM


Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained.

This is a preview of subscription content, access via your institution.


  1. 1.

    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: Part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  2. 2.

    Ferreira, F., Amorim, I., Rocha, R., Dias, J.: T-SLAM: Registering Topological and Geometric Maps for Robot Localization in Large Environments. In: Hahn, H., Ko, H., Lee, S. (eds.) Multisensor Fusion and Integration for Intelligent Systems, pp 423–438. Springer (2009)

  3. 3.

    Lazaro, M., Paz, L., Pinies, P., Castellanos, J., Grisetti, G.: Multi-Robot SLAM Using Condensed Measurements.. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp 1069–1076 (2013)

  4. 4.

    Rocha, R.P., Portugal, D., Couceiro, M.S., Araújo, F., Menezes, P., Lobo, J.: The CHOPIN project: Cooperation between Human and rObotic teams in catastroPhic INcidents.. In: IEEE 13th International Symposium on Safety, Security and Rescue Robotics. Linköping, Sweden (2013)

  5. 5.

    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)

  6. 6.

    Julier, S.J., Uhlmann, J.K.: A counter example to the theory of simultaneous localization and map building.. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, 2001., vol. 4, pp 4238–4243 (2001)

  7. 7.

    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem.. In: AAAI National Conference on Artificial Intelligence. Edmonton, Canada (2002)

  8. 8.

    Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the Simultaneous Localization and Map Building (SLAM) problem.. In: IEEE Transactions on Robotics and Automation, vol. 17, pp 229–241 (2001)

  9. 9.

    Huang, S., Dissanayake, G.: Convergence and consistency analysis for extended Kalman filter based SLAM. In: IEEE Transactions on Robotics, vol. 23, pp 1036–1049 (2007)

  10. 10.

    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters.. In: Transactions on Robotics, vol. 23, pp 34–46 (2007)

  11. 11.

    Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping.. In: Autonomous Robots, Vol. 4, pp 333–349 (1997)

  12. 12.

    Agarwal, P., Tipaldi, G.D., Spinello, L., Stachniss, C., Burgard, W.: Robust map optimization using dynamic covariance scaling.. In: 2013 IEEE International Conference on Robotics and Automation, pp 62–69. Karlsruhe, Germany (2013)

  13. 13.

    Kohlbrecher, S., Meyer, J., Von Stryk, O., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation.. In: 11th IEEE International Symposium on Safety, Security and Rescue Robotics. Kyoto, Japan (2011)

  14. 14.

    Pedrosa, E., Lau, N., Pereira, A.: Online SLAM based on a fast scan-matching algorithm. In: Correia, L., Reis, L. P., Cascalho, J. (eds.) Progress in Artificial Intelligence, Lecture Notes in Computer Science, Vol. 8154, pp 295–306. Springer (2013)

  15. 15.

    Brunner, C., Peynot, T., Vidal-Calleja, T.: Automatic selection of sensor modality for resilient localisation in low visibility conditions.. In: 2012 Robotics: Science and Systems, Workshop on Beyond laser and vision: Alternative sensing techniques for robotic perception. Sydney, Australia (2012)

  16. 16.

    Deissler, T., Thielecke, J.: UWB SLAM with Rao-Blackwellized Monte Carlo data association.. In: International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, pp 1–5 (2010)

  17. 17.

    Castro, M., Peynot, T.: Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions.. In: Australasian Conference on Robotics and Automation, Sydney, Australia (2012)

  18. 18.

    Sales, J., Marín, R., Cervera, E., Rodríguez, S., Pérez, J.: Multi-sensor person following in low-visibility scenarios. Sensors 10(12), 10953–10966 (2010)

    Article  Google Scholar 

  19. 19.

    Marti, J., Sales, J., Marín, R., Sanz, P.. In: International Journal of Advanced Robotic Systems, vol. 10(211) (2011)

  20. 20.

    Pascoal, J., Marques, L., De Almeida, A.: Assessment of laser range finders in risky environments. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp 3533–3538 (2008)

  21. 21.

    Tretyakov, V., Linder, T. : Range sensors evaluation under smoky conditions for robotics applications.. In: 11th IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, Japan, pp 215–220 (2011)

  22. 22.

    Pomerleau, F., Breitenmoser, A., Liu, M., Colas, F., Siegwart, R.: Noise characterization of depth sensors for surface inspections. (2012)

  23. 23.

    Steux, B., El Hamzaoui, O.: tinySLAM: a SLAM algorithm in less than 200 lines C-Language program.. In: 2010 11th International Conference on Control Automation Robotics & Vision, Singapore, pp 1975–1979 (2010)

  24. 24.

    Carlone, L., Aragues, R., Castellanos, J.A., Bona, B.: A linear approximation for graph-based simultaneous localization and mapping. In: Robotics: Science and Systems, Los Angeles, California, USA (2011)

  25. 25.

    Vincent, R., Limketkai, B., Eriksen, M.: Comparison of indoor robot localization techniques in the Absence of GPS.. In: SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76641Z (2010)

  26. 26.

    Machado Santos, J., Portugal, D., Rocha, R.P.: An evaluation of 2D SLAM techniques available in robot operating system.. In: IEEE 13th International Symposium on Safety, Security and Rescue Robotics. Linköping, Sweden (2013)

  27. 27.

    Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: 2009 IEEE International Conference on Robotics and Automation, Workshop on Open Source Software, Kobe, Japan (2009)

  28. 28.

    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)

    Article  Google Scholar 

  29. 29.

    Couceiro, M.S., Machado, J.T., Rocha, R.P., Ferreira, N.M.: A fuzzified systematic adjustment of the robotic Darwinian PSO. Robot. Auton. Syst. 60(12), 1625–1639 (2012)

    Article  Google Scholar 

  30. 30.

    Ferreira, J.F., Dias, J.: Probabilistic approaches to robotic perception. Springer Tracts in Advanced Robotics, 91 (2014)

  31. 31.

    Zadeh, L.A.: Fuzzy sets information and control. Inf. Control. 8(3), 338–353 (1965)

    MATH  MathSciNet  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Rui P. Rocha.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Santos, J.M., Couceiro, M.S., Portugal, D. et al. A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM. J Intell Robot Syst 80, 401–422 (2015).

Download citation


  • SLAM
  • Reduced visibility
  • Sensor fusion
  • Robot Operating System (ROS)