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Autonomous Guidance for a UAS Along a Staircase

  • Olivier De Meyst
  • Thijs Goethals
  • Haris Balta
  • Geert De CubberEmail author
  • Rob Haelterman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)

Abstract

In the quest for fully autonomous unmanned aerial systems (UAS), multiple challenges are faced. For enabling autonomous UAS navigation in indoor environments, one of the major bottlenecks is the capability to autonomously traverse narrow 3D - passages, like staircases. This paper presents a novel integrated system that implements a semi-autonomous navigation system for a quadcopter. The navigation system permits the UAS to detect a staircase using only the images provided by an on-board monocular camera. A 3D model of this staircase is then automatically reconstructed and this model is used to guide the UAS to the top of the detected staircase. For validating the methodology, a proof of concept is created, based on the Parrot AR.Drone 2.0 which is a cheap commercial off-the-shelf quadcopter.

Keywords

Extend Kalman Filter Agglomerative Cluster Autonomous Navigation Unmanned Aerial System Robot Operating System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 285417 (ICARUS).

References

  1. 1.
    De Cubber, G., Doroftei, D., Serrano, D., Chintamani, K., Sabino, R., Ourevitch, S.: The eu-icarus project: developing assistive robotic tools for search and rescue operations. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, Sweden. IEEE, RAS (2013)Google Scholar
  2. 2.
    Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., Siegwart, R.: A UAV system for inspection of industrial facilities. In: 2013 IEEE Aerospace Conference, pp. 1–8 (2013)Google Scholar
  3. 3.
    Yamauchi, B., Rudakevych, P.: Griffon: a man portable hybrid UGV/UAV. Ind. Robot Int. J. 31, 443–450 (2004)CrossRefGoogle Scholar
  4. 4.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings on Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan (2007)Google Scholar
  5. 5.
    Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: Large-Scale Direct Monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014) Google Scholar
  6. 6.
    Lu, X., Manduchi, R.: Detection and localization of curbs and stairways using stereo vision. Int. Conf. Robot. Autom. (ICRA) 4, 4648 (2005)Google Scholar
  7. 7.
    Lee, Y.H., Leung, T.S., Medioni, G.: Real-time staircase detection from a wearable stereo system. In: International Conference on Pattern Recognition, pp. 3770–3773 (2012)Google Scholar
  8. 8.
    Delmerico, J.A., Baran, D., David, P., Ryde, J., Corso, J.J.: Ascending stairway modeling from dense depth imagery for traversability analysis, pp. 2283–2290 (2013)Google Scholar
  9. 9.
    Pérez-Yus, A., López-Nicolás, G., Guerrero, J.J.: Detection and modelling of staircases using a wearable depth sensor. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8927, pp. 449–463. Springer, Heidelberg (2015) Google Scholar
  10. 10.
    Se, S., Brady, M.: Vision-based detection of staircases, vol. 1, pp. 535–540 (2000)Google Scholar
  11. 11.
    Bills, C., Chen, J., Saxena, A.: Autonomous mav flight in indoor environments using single image perspective cues. In: International Conference on Robotics and Automation (ICRA) (2011)Google Scholar
  12. 12.
    Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: Lsd: a line segment detector. Image Process. Line 2, 5 (2012)Google Scholar
  13. 13.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. 4, 34–47 (2001)Google Scholar
  14. 14.
    Saberian, M., Vasconcelos, N.: Boosting algorithms for detector cascade learning. J. Mach. Learn. Res. 15, 2569–2605 (2014)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Foley, J.D., Van Dam, A., Feiner, S.K., Hughes, J.F., Phillips, R.L.: Introduction to Computer Graphics, vol. 55. Addison-Wesley, Reading (1994) zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Olivier De Meyst
    • 1
  • Thijs Goethals
    • 1
  • Haris Balta
    • 1
  • Geert De Cubber
    • 1
    Email author
  • Rob Haelterman
    • 1
  1. 1.Royal Military AcademyBrusselsBelgium

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