Skip to main content
Log in

A Real-time Door Detection System for Domestic Robotic Navigation

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Human beings use doors to access rooms and corridors, to know where they are, to know where they have to go, etc. Similarly, it would be quite useful for robots to be able to detect doors in order to accomplish more complex and flexible navigation tasks. Such a goal is even more desirable when domestic environments are taken into account. Moreover, if the human-robot interaction is considered, the use of this semantic information can be broadly used. In this paper we present a solid and complete door detection system which fuses data from an end-user camera and a laser rangefinder. By using both Haar-like features and the Integral Image, the computation time is significantly reduced when compared to other methods found in the literature. Extensive tests in real-world environments have been performed in order to prove the efficiency, robustness and real-time ability of our system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. AENOR, Spanish Normalization Agency: UNE 56801:2008, Unidad de hueco de puerta de madera. Terminología, definiciones y clasificación. COMITE AEN/CTN 56 - MADERA Y CORCHO (2008)

  2. Kosaka, A., Kak, A.C.: Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties. CVGIP: Image Underst. 56(3), 271–329 (1992)

    Article  MATH  Google Scholar 

  3. Murillo, A.C., Kosecká, J., Guerrero, J.J., Sagüés, C.: Visual door detection integrating appearance and shape cues. Robot. Auton. Syst. 56(6), 512–521 (2008)

    Article  Google Scholar 

  4. Fernández-Caramés, C., Moreno, V., Curto, B., Vicente, J.A.: Clustering and line detection in laser range measurements. Robot. Auton. Syst. 58, 720–726 (2010)

    Article  Google Scholar 

  5. Fernández-Caramés, C.: PhD Thesis: Técnicas de navegación para un robot móvil utilizando sistemas de razonamiento espacial. Directed by: V. Moreno, B. Curto, Universidad de Salamanca, Salamanca (2012)

  6. Guan, C.-H., Gong, J.-W., Chen, Y.-D., Chen, H.-Y.: An application of data fusion combining laser scanner and vision in real-time driving environment recognition system. In: International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3116–3121 (2009)

  7. Madsen, C.B., Andersen, C.S.: Optimal landmark selection for triangulation of robot position. Robot. Auton. Syst. 23(4), 277–292 (1998)

    Article  Google Scholar 

  8. Kim, D., Nevatia, R.: Recognition and localization of generic objects for indoor navigation using functionality. Image Vis. Comput. 16(11), 729–743 (1998)

    Article  Google Scholar 

  9. Anguelov, D., Koller, D., Parker, E., Thrun, S.: Detecting and modeling doors with mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3777–3784 (2004)

  10. Krotkov, E.: Mobile robot localization using a single image. In: Proceedings of the 1989 IEEE International Conference on Robotics and Automation, vol. 2, pp. 978–983 (1989)

  11. Monasterio, I., Lazkano, E., Rañó, , I., Sierra, B.: Learning to traverse doors using visual information. Math. Comput. Simul. 60(3), 347–356 (2002)

    Article  MATH  Google Scholar 

  12. Brian Burns, J., Hanson, A.R., Riseman, E.M.: Extracting straight lines. IEEE Trans. Pattern Anal. Mach. Intell. 8(4), 425–455 (1986)

    Article  Google Scholar 

  13. Crowley, J.L., Chenavier, F.: Position estimation for a mobile robot using vision and odometry. In: Proceedings of the 1992 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2588–2593 (1992)

  14. Hensler, J., Blaich, M., Bittel, O.: Real-time door detection based on adaboost learning algorithm. In: Research and Education in Robotics - EUROBOT 2009. Communications in Computer and Information Science, vol. 82, pp. 61–73 (2010)

  15. Gutmann, J.-S., Burgard, W., Fox, D., Konolige, K.: An experimental comparison of localization methods. In: Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 736–743 (1998)

  16. Castellanos, J.A., Neira, J., Strauss, O., J.D. Tardós: Detecting high level features for mobile robot localization. In: IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1996, pp. 611–618 (1996)

  17. Neira, J., Tardós, J.D., Horn, J., Schmidt, G.: Fusing range and intensity images for mobile robot localization. IEEE Trans. Robot. Autom. 15(1), 76–84 (1999)

    Article  Google Scholar 

  18. Neira, J., Ribeiro, M.I., Tardós, J.D.: Mobile robot localization and map building using monocular vision. In: Proceedings of the 5th International Symposium on Intelligent Robotic Systems, pp. 275–284, Stockholm, Sweden (1997)

  19. Tardos, J.D.: Representing partial and uncertain sensorial information using the theory of symmetries. In: Proceedings of the 1992 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1799–1804 (1992)

  20. Arras, K.O., Tomatis, N.: Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision. In: Proceedings of the 1999 Third European Workshop on Advanced Mobile Robots, pp. 177–185 (1999)

  21. Arras, K.O., Tomatis, N., Jensen, B., Siegwart, R.: Multisensor on-the-fly localization: precision and reliability for applications. Robot. Auton. Syst. 34(2–3), 131–143 (2001)

    Article  MATH  Google Scholar 

  22. Kisačanin, B.: Integral image optimizations for embedded vision applications. In: IEEE Southwest Symposium on Image Analysis and Interpretation 2008, pp. 181–184 (2008)

  23. Sugihara, K.: Some location problems for robot navigation using a single camera. Comput. Vis. Graph. Image Process. 42(1), 112–129 (1988)

    Article  Google Scholar 

  24. Shi, L., Kodagoda, S., Dissanayake, G.: Laser range data based semantic labeling of places. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5941–5946 (2010)

  25. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  26. Cariñena, P., Regueiro, C.V., Otero, A., Bugarín, A.J., Barro, S.: Landmark detection in mobile robotics using fuzzy temporal rules. IEEE Trans. Fuzzy Syst. 12(4), 423–435 (2004)

    Article  Google Scholar 

  27. Barber, R., Mata, M., Boada, M.J.L., Armingol, J.M., Salichs, M.A.: A perception system based on laser information for mobile robot topologic navigation. In: Proceedings of the IEEE 28th Annual Conference of the Industrial Electronics Society, vol. 4, pp. 2779–2784 (2002)

  28. Rusu, R.B., Meeussen, W., Chitta, S., Beetz, M.: Laser-based perception for door and handle identification. In: Proceedings of the International Conference on Advanced Robotics, pp. 1–8 (2009)

  29. Muñoz-Salinas, R., Aguirre, E., García-Silvente, M., González, A.: Door-detection using computer vision and fuzzy logic. WSEAS Trans. Syst. 10(3), 3047–3052 (2004)

    Google Scholar 

  30. Atiya, S., Hager, G.D.: Real-time vision-based robot localization. IEEE Trans. Robot. Autom. 9(6), 785–800 (1993)

    Article  Google Scholar 

  31. Thrun, S.: Robotic mapping: a survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millennium, pp. 1–36. Morgan Kaufmann, San Mateo, CA (2002)

    Google Scholar 

  32. Yang, X., Tian, Y.: Robust door detection in unfamiliar environments by combining edge and corner features. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 57–64 (2010)

  33. Weiß, G., Wetzler, C., von Putkammer, E.: Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. In: Proceedings of the International Conference on Intelligent Robots and Systems (1994)

  34. Shi, W., Samarabandu, J.: Investigating the performance of corridor and door detection algorithms in different environments. In: International Conference on Information and Automation, pp. 206–211 (2006)

  35. Chen, Z., Birchfield, S.T.: Visual detection of linteloccluded doors from a single image. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)

  36. Chen, Z., Li, Y., Birchfield, S.T.: Visual detection of lintel-occluded doors by integrating multiple cues using a data-driven Markov chain Monte Carlo process. Robot. Auton. Syst. 59(11), 966–976 (2011). ISSN 0921-8890

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. Moreno.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fernández-Caramés, C., Moreno, V., Curto, B. et al. A Real-time Door Detection System for Domestic Robotic Navigation. J Intell Robot Syst 76, 119–136 (2014). https://doi.org/10.1007/s10846-013-9984-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9984-6

Keywords

Navigation