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Visual 3-D SLAM from UAVs

  • Jorge Artieda
  • José M. Sebastian
  • Pascual Campoy
  • Juan F. Correa
  • Iván F. MondragónEmail author
  • Carol Martínez
  • Miguel Olivares
Article

Abstract

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs.

Keywords

Computer vision Visual SLAM Unmanned aerial vehicles (UAV) 3D SLAM 

References

  1. 1.
    Se, S., Barfoot, T., Jasiobedzki, P.: Visual motion estimation and terrain modeling for planetary rovers. In: Proceedings of ISAIRAS (1995)Google Scholar
  2. 2.
    Sim, R., Elinas, P., Griffin, M., Little, J.J.: Vision-based SLAM using the Rao-Blackwellised particle filter. In: IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR) (2005)Google Scholar
  3. 3.
    Davison, A.J., Reid, I., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  4. 4.
    Munguia, R., Grau, A.: Monocular slam for visual odometry. In: IEEE International Symposium on Intelligent Signal Processing, 2007. WISP 2007, pp. 1–6. IEEE, Piscataway (2007)CrossRefGoogle Scholar
  5. 5.
    Kim, S., Oh, S.-Y.: Slam in indoor environments using omni-directional vertical and horizontal line features. J. Intell. Robot. Syst. 51(1), 31–43 (2008)CrossRefGoogle Scholar
  6. 6.
    Choi, Y.-H., Oh, S.-Y.: Grid-based visual slam in complex environments. J. Intell. Robot. Syst. 50(3), 241–255 (2007)CrossRefGoogle Scholar
  7. 7.
    Montiel, J.M.M., Civera, J., Davison, A.J.: Unified inverse depth parametrization for monocular slam. In: Robotics: Science and Systems (2006)Google Scholar
  8. 8.
    Ho, K.L., Newman, P.: Detecting loop closure with scene sequences. Int. J. Comput. Vis. 74(3), 261–286 (2007)CrossRefGoogle Scholar
  9. 9.
    Lemaire, T., Berger, C., Jung, I., Lacroix, S.: Vision-based SLAM: stereo and monocular approaches. Int. J. Comput. Vis. 74(3), 343–364 (2007)CrossRefGoogle Scholar
  10. 10.
    Dailey, M., Parnichkun, M.: Simultaneous localization and mapping with stereo vision. In: Proceedings of the IEEE International Conference on Automation, Robotics, and Computer Vision (ICARCV) (2006)Google Scholar
  11. 11.
    Klippenstein, J., Zhang, H.: Quantitative evaluation of feature extractors for visual slam. In: Fourth Canadian Conference on Computer and Robot Vision, 2007. CRV ’07., pp. 157–164 (2007)Google Scholar
  12. 12.
    Lee, Y.-J., Song, J.-B.: Autonomous selection, registration, and recognition of objects for visual slam in indoor environments. In: Fourth Canadian Conference on Computer and Robot Vision, 2007. CRV ’07., pp. 668–673 (2007)Google Scholar
  13. 13.
    Mikolajczyk, M., Smid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  14. 14.
    Törnqvist, D., Conte, G., Kärlsson, R., Schon, T.B., Gustafsson, F.: Utilizing model structure for efficient simultaneous localization and mapping for a uav application. In: Proceeding of the IEEE Aerospace Conference (2008)Google Scholar
  15. 15.
    Kim, J., Sukkarieh, S.: Real-time implementation of airborne inertial-slam. Robot. Auton. Syst. 55(1), 62–71 (2007)CrossRefGoogle Scholar
  16. 16.
    McLain, T.W., Beard, R.W., Barber, D.B., Redding, J.D., Taylor, C.N.: Vision-based target geo-location using a fixed-wing miniature air vehicle. J. Intell. Robot. Syst. 47(4), 361–382 (2006)CrossRefGoogle Scholar
  17. 17.
    Tsourdos, A., Aouf, N., Sazdovski, V., White, B.: Low altitude airbone slam with ins aided vision system. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, South Carolina, AIAA (2007)Google Scholar
  18. 18.
    Mejías, L., Mondragón, I., Correa, J.F., Campoy, P.: Colibri: vision-guided helicopter for surveillance and visual inspection. In: Video Proceedings of IEEE International Conference on Robotics and Automation, Rome, April 2007Google Scholar
  19. 19.
    Mejias, L.: Control visual de un vehiculo aereo autonomo usando detección y seguimiento de características en espacios exteriores. Ph.D. thesis, Escuela Técnica Superior de Ingenieros Industriales. Universidad Politécnica de Madrid, Madrid, December 2006Google Scholar
  20. 20.
    Mejias, L., Saripalli, S., Campoy, P., Sukhatme, G.: Visual servoing of an autonomous helicopter in urban areas using feature tracking. J. Field Robot. 23(3–4), 185–199 (2006)CrossRefGoogle Scholar
  21. 21.
    Mejias, L., Campoy, P., Mondragon, I., Doherty, P.: Stereo visual system for autonomous air vehicle navigation. In: 6th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 07), Toulouse, September 2007Google Scholar
  22. 22.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Proceedings of the Ninth European Conference on Computer Vision, May (2006)Google Scholar
  23. 23.
    Lowe, D.G.: Distintive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  24. 24.
    Fontanelli, D., Danesi, A., Bicchi, A.: Visual servoing on image maps. In: Springer Tracts in Advanced Robotics. Experimental Robotics, vol. 39. Springer, New York (2008)Google Scholar
  25. 25.
    Harris, C.G., Stephens, M.: A combined corner and edge detection. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  26. 26.
    Parra, I., Fernández, D., Naranjo, J.E., García-García, R., Sotelo, M.A., Gavilán, M.: 3d visual odometry for road vehicles. J. Intell. Robot. Syst. 51(1), 113–134 (2008)CrossRefGoogle Scholar
  27. 27.
    Mozos, O.M., Gil, A., Ballesta, M., Reinoso, O.: In: Lecture Notes in Computer Science, Current Topics in Artificial Intelligence, Chapter Interest Point Detectors for Visual SLAM, vol. 4788, pp. 170–179. Springer, Berlin (2008)Google Scholar
  28. 28.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  29. 29.
    OpenCV: Open Source Computer Vision Library OpenCV. http://www.intel.com/research/mrl/research/opencv/ (2001)
  30. 30.
    Wiklund, J., Caballero, F., Moe, A., De Dios, J.R.M., Forssen, P.-E., Nordberg, K., Ollero, A., Merino, L.: Vision-based multi-uav position estimation. In: Robotics And Automation Magazine, vol. 13, September 2006Google Scholar
  31. 31.
    Carmi, A., Oshman, Y.: On the covariance singularity of quaternion estimators. In: AIAA Guidance, Navigation and Control Conference, Hilton Head, South Carolina (Paper No. AIAA-2007-6814), 20–23 August 2007Google Scholar
  32. 32.
    Civera, J., Davison, A.J., Montiel, J.M.M.: Dimensionless monocular slam. In: IbPRIA, pp. 412–419 (2007)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Jorge Artieda
    • 1
  • José M. Sebastian
    • 1
  • Pascual Campoy
    • 1
  • Juan F. Correa
    • 1
  • Iván F. Mondragón
    • 1
    Email author
  • Carol Martínez
    • 1
  • Miguel Olivares
    • 1
  1. 1.Computer Vision GroupU.P.M.MadridSpain

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