Advertisement

Real-Time Accurate Geo-Localization of a MAV with Omnidirectional Visual Odometry and GPS

  • Johannes SchneiderEmail author
  • Wolfgang Förstner
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

This paper presents a system for direct geo-localization of a MAV in an unknown environment using visual odometry and precise real time kinematic (RTK) GPS information. Visual odometry is performed with a multi-camera system with four fisheye cameras that cover a wide field of view which leads to better constraints for localization due to long tracks and a better intersection geometry. Visual observations from the acquired image sequences are refined with a high accuracy on selected keyframes by an incremental bundle adjustment using the iSAM2 algorithm. The optional integration of GPS information yields long-time stability and provides a direct geo-referenced solution. Experiments show the high accuracy which is below 3 cm standard deviation in position.

Keywords

Visual odometry Incremental bundle adjustment Fisheye camera Multi-camera system Omnidirectional MAV 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, S., Förstner, W.: Fish-eye-stereo calibration and epipolar rectification. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 59(5), 278–288 (August 2005)Google Scholar
  2. 2.
    Abraham, S., Hau, T.: Towards autonomous high-precision calibration of digital cameras. In: Proc. of SPIE Videometrics, vol. 3174, pp. 82–93, San Diego, USA, July 1997Google Scholar
  3. 3.
    Aliaga, D.: Accurate catadioptric calibration for real-time pose estimation of room-size environments. In: Proc. of the IEEE Intl. Conf. on Computer Vision (ICCV), pp. 127–134, Vancouver, Canada, July 2001Google Scholar
  4. 4.
    Bouguet, J.Y.: Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs, Tech. rep. (2000)Google Scholar
  5. 5.
    Eling, C., Klingbeil, L., Wieland, M., Kuhlmann, H.: Direct georeferencing of micro aerial vehicles - system design, system calibration and first evaluation tests. Photogrammetrie - Fernerkundung - Geoinformation (PFG) (4), August 2014Google Scholar
  6. 6.
    Engels, C., Stewénius, H., Nistér, D.: Bundle adjustment rules. In: Proc. of the ISPRS Conference on Photogrammeric Computer Vision (PCV), Bonn, Germany, September 2006Google Scholar
  7. 7.
    Huber, P.: Robust Statistics. John Wiley, New York (1981)Google Scholar
  8. 8.
    Kaess, M., Dellaert, F.: Visual SLAM with a multi-camera rig. Tech. Rep. GIT-GVU-06-06, Georgia Institute of Technology, February 2006Google Scholar
  9. 9.
    Kaess, Michael, Ila, Viorela, Roberts, Richard, Dellaert, Frank: The Bayes tree: an algorithmic foundation for probabilistic Robot mapping. In: Hsu, David, Isler, Volkan, Latombe, Jean-Claude, Lin, Ming C. (eds.) Algorithmic Foundations of Robotics IX. STAR, vol. 68, pp. 157–173. Springer, Heidelberg (2010) Google Scholar
  10. 10.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J., Dellaert, F.: iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree. Intl. Journal of Robotics Research (IJRR) 31(2), 217–236 (February 2012)Google Scholar
  11. 11.
    Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: Proc. of the Intl. Symposium on Mixed and Augmented Reality (ISMAR), pp. 1–10, Nara, Japan, November 2007Google Scholar
  12. 12.
    Klingbeil, L., Nieuwenhuisen, M., Schneider, J., Eling, C., Dröschel, D., Holz, D., Läbe, T., Förstner, W., Behnke, S., Kuhlmann, H.: Towards autonomous navigation of an uav-based mobile mapping system. In: Proc. of the Intl. Conf. on Machine Control and Guidance (MCG), pp. 136–147, Brunswick, Germany, March 2014Google Scholar
  13. 13.
    Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: A general framework for graph optimization. In: Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), pp. 3607–3613, Shanghai, China, May 2011Google Scholar
  14. 14.
    Maas, H.: Image sequence based automatic multi-camera system calibration techniques. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 54(5–6), 352–359 (December 1999)Google Scholar
  15. 15.
    Mostafa, M., Schwarz, K.: Digital image georeferencing from a multiple camera system by gps/ins. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 56(1), 1–12 (June 2001)Google Scholar
  16. 16.
    Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: Generic and real-time structure from motion using local bundle adjustment. Elsevier Journal on Image and Vision Computing (IVC) 27(8), 1178–1193 (July 2009)Google Scholar
  17. 17.
    Nieuwenhuisen, M., Dröschel, D., Schneider, J., Holz, D., Läbe, T., Behnke, S.: Multimodal obstacle detection and collision avoidance for micro aerial vehicles. In: Proc. of the European Conf. on Mobile Robotics (ECMR), pp. 7–12, Barcelona, Spain, September 2013Google Scholar
  18. 18.
    Schneider, J., Förstner, W.: Bundle adjustment and system calibration with points at infinity for omnidirectional camera systems. Photogrammetrie - Fernerkundung - Geoinformation (PFG) 4, 309–321 (August 2013)Google Scholar
  19. 19.
    Schneider, J., Läbe, T., Förstner, W.: Incremental real-time bundle adjustment for multi-camera systems with points at infinity. In: ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-1/W2, pp. 355–360, Rostock, Germany, September 2013Google Scholar
  20. 20.
    Schneider, J., Schindler, F., Läbe, T., Förstner, W.: Bundle adjustment for multi-camera systems with points at infinity. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. I–3, pp. 75–80, Melbourne, Australia, August 2012Google Scholar
  21. 21.
    Shi, J., Tomasi, C.: Good features to track. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 593–600, Seattle, USA, June 1994Google Scholar
  22. 22.
    Strasdat, H., Montiel, J., Davison, A.: Visual slam: Why filter? Elsevier Journal on Image and Vision Computing (IVC) 30(2), 65–77 (February 2012)Google Scholar
  23. 23.
    Tardif, J.P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 2531–2538, Nice, France, September 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of PhotogrammetryUniversity of BonnBonnGermany

Personalised recommendations

Cite paper

Buy options