Movement Direction Estimation Using Omnidirectional Images in a SLAM Algorithm

  • Yerai BerenguerEmail author
  • Luis Payá
  • Oscar Reinoso
  • Adrián Peidró
  • Luis Miguel Jiménez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 693)


This work presents a method to estimate the movement direction of a mobile robot using only visual information, without any other additional sensor. This visual information is provided by a catadioptric system mounted on the robot and formed by a camera pointing towards a convex mirror. It provides the robot with omnidirectional images that contain information with a field of view of 360\(^\circ \) around the camera-mirror axis. A SLAM algorithm is presented to test the method that estimates the movement direction of the robot. This SLAM method uses two different global appearance descriptors to calculate the orientation of the robot and the distance between two different positions. The method to calculate the movement direction is based on landmarks extraction, using SURF features. A set of omnidirectional images has been considered to test the effectiveness of this method.


SLAM Omnidirectional images Vision systems Image description 



This work has been supported by the Spanish Government through the project DPI2016-78361-R (AEI/FEDER, UE) “Creación de Mapas Mediante Métodos de Apariencia Visual para la Navegación de Robots”.


  1. 1.
    Bay, H., Tuytelaars, T., Gool, L.: Surf: speeded up robust features. In: Computer Vision at ECCV, vol. 3951, pp. 404–417 (2006)Google Scholar
  2. 2.
    Berenguer, Y., Payá, L., Ballesta, M., Reinoso, O.: Position estimation and local mapping using omnidirectional images and global appearance descriptors. Sensors 15(10), 26368 (2015)CrossRefGoogle Scholar
  3. 3.
    Chang, C., Siagian, C., Itti, L.: Mobile robot vision navigation and localization using gist and saliency. In: IROS 2010, International Conference on Intelligent Robots and Systems, pp. 4147–4154 (2010)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005Google Scholar
  5. 5.
    Fernández, L., Payá, L., Reinoso, O., Jiménez, L., Ballesta, M.: A study of visual descriptors for outdoor navigation using google street view images. J. Sens. 2016 (2016)Google Scholar
  6. 6.
    Garcia-Fidalgo, E., Ortiz, A.: Vision-based topological mapping and localization methods: a survey. Robot. Auton. Syst. 64, 1–20 (2015)CrossRefGoogle Scholar
  7. 7.
    Hasegawa, M., Tabbone, S.: A shape descriptor combining logarithmic-scale histogram of radon transform and phase-only correlation function. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 182–186, September 2011Google Scholar
  8. 8.
    Hoang, T., Tabbone, S.: A geometric invariant shape descriptor based on the radon, fourier, and mellin transforms. In: 20th International Conference on Pattern Recognition (ICPR), pp. 2085–2088, August 2010Google Scholar
  9. 9.
    Kobayashi, K., Aoki, T., Ito, K., Nakajima, H., Higuchi, T.: A fingerprint matching algorithm using phase-only correlation. IEICE Trans. Fundam. Electr. Commun. Comput. Sci. E87–A, 682–691 (2004)Google Scholar
  10. 10.
    Kuglin, C., Hines, D.: The phase correlation image alignment method. In: Proceedings of the IEEE, International Conference on Cybernetics and Society, pp. 163–165 (1975)Google Scholar
  11. 11.
    Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: a general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3607–3613, May 2011Google Scholar
  12. 12.
    Lowe, D.: Object recognition from local scale-invariant features. In: ICCV 1999, International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Payá, L., Amorós, F., Fernández, L., Reinoso, O.: Performance of global-appearance descriptors in map building and localization using omnidirectional vision. Sensors 14(2), 3033–3064 (2014)CrossRefGoogle Scholar
  14. 14.
    Payá, L., Fernández, L., Gil, L., Reinoso, O.: Map building and monte carlo localization using global appearance of omnidirectional images. Sensors 10(12), 11468–11497 (2010)CrossRefGoogle Scholar
  15. 15.
    Radon, J.: Uber die bestimmung von funktionen durch ihre integralwerte langs gewisser mannigfaltigkeiten. Berichte Sachsische Akademie der Wissenschaften 69(1), 262–277 (1917)zbMATHGoogle Scholar
  16. 16.
    Valiente, D., Gil, A., Fernández, L., Reinoso, O.: A comparison of EKF and SGD applied to a view-based SLAM approach with omnidirectional images. Robot. Auton. Syst. 62(2), 108–119 (2014)CrossRefGoogle Scholar
  17. 17.
    Winters, N., Gaspar, J., Lacey, G., Santos-Victor, J.: Omni-directional vision for robot navigation. In: IEEE Workshop on Omnidirectional Vision, pp. 21–28 (2000)Google Scholar
  18. 18.
    Wu, J., Zhang, H., Guan, Y.: An efficient visual loop closure detection method in a map of 20 million key locations. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 861–866, May 2014Google Scholar
  19. 19.
    Zhu, Q., Yeh, M.-C., Cheng, K.-T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1491–1498 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yerai Berenguer
    • 1
    Email author
  • Luis Payá
    • 1
  • Oscar Reinoso
    • 1
  • Adrián Peidró
    • 1
  • Luis Miguel Jiménez
    • 1
  1. 1.Departamento de Ingeniería de Sistemas y AutomáticaMiguel Hernández UniversityAlicanteSpain

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