How Computer Vision Can Help in Outdoor Positioning

  • Ulrich Steinhoff
  • Dušan Omerčević
  • Roland Perko
  • Bernt Schiele
  • Aleš Leonardis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4794)


Localization technologies have been an important focus in ubiquitous computing. This paper explores an underrepresented area, namely computer vision technology, for outdoor positioning. More specifically we explore two modes of positioning in a challenging real world scenario: single snapshot based positioning, improved by a novel high-dimensional feature matching method, and continuous positioning enabled by combination of snapshot and incremental positioning. Quite interestingly, vision enables localization accuracies comparable to GPS. Furthermore the paper also analyzes and compares possibilities offered by the combination of different subsets of positioning technologies such as WiFi, GPS and dead reckoning in the same real world scenario as for vision based positioning.


computer vision based positioning local invariant features sensor fusion for outdoor localization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hightower, J., Borriello, G.: Location Systems for Ubiquitous Computing. Computer 34(8), 57–66 (2001)CrossRefGoogle Scholar
  2. 2.
    Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age. Computer 37(2), 95–97 (2004)CrossRefGoogle Scholar
  3. 3.
    Cheng, Y.C., Chawathe, Y., LaMarca, A., Krumm, J.: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization. In: MobiSys (2005)Google Scholar
  4. 4.
    Varshavsky, A., Chen, M.Y., de Lara, E., Froehlich, J., Haehnel, D., Hightower, J., LaMarca, A., Potter, F., Sohn, T., Tang, K., Smith, I.: Are GSM Phones THE Solution for Localization? In: WMCSA 2006 (2006)Google Scholar
  5. 5.
    Zhang, W., Košecká, J.: Image based localization in urban environments. In: International Symposium on 3D Data Processing, Visualization and Transmission, pp. 33–40 (2006)Google Scholar
  6. 6.
    Omerčević, D., Drbohlav, O., Leonardis, A.: High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors. In: ICCV (2007)Google Scholar
  7. 7.
    Johansson, B., Cipolla, R.: A system for automatic pose-estimation from a single image in a city scene. In: Proc. of International Conference on Signal Processing, Pattern Recognition, and Applications (2002)Google Scholar
  8. 8.
    Robertson, D., Cipolla, R.: An image-based system for urban navigation. In: BMVC, pp. 819–828 (2004)Google Scholar
  9. 9.
    Zhang, W., Košecká, J.: Hierarchical building recognition. Image and Vision Computing 25(5), 704–716 (2007)CrossRefGoogle Scholar
  10. 10.
    Yeh, T., Tollmar, K., Darrell, T.: Searching the web with mobile images for location recognition. In: CVPR, vol. 2, pp. 76–81 (2004)Google Scholar
  11. 11.
    Bahl, P., Padmanabhan, V.N.: RADAR: An In-Building RF-based User Location and Tracking System. In: IEEE Infocom 2000, IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  12. 12.
    Ekahau. Online
  13. 13.
    LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.: Place Lab: Device Positioning Using Radio Beacons in the Wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Skyhook Wireless. Online
  15. 15.
    Navizon: Peer-to-Peer Wireless Positioning. Online
  16. 16.
    Judd, T.: A Personal Dead Reckoning Module. In: ION GPS 1997 (1997)Google Scholar
  17. 17.
    Macheiner, K.: Performance Analysis of a Commercial Multi-Sensor Pedestrian Navigation System. Master’s thesis, IGMS, Graz University of Technology (September 2004)Google Scholar
  18. 18.
    Ladetto, Q., Merminod, B.: Digital Magnetic Compass and Gyroscope Integration for Pedestrian Navigation. In: 9th Saint Petersburg International Conference on Integrated Navigation Systems (2002)Google Scholar
  19. 19.
    Vectronix. Online
  20. 20.
    Randell, C., Djiallis, C., Muller, H.L.: Personal Position Measurement Using Dead Reckoning. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, Springer, Heidelberg (2003)Google Scholar
  21. 21.
    Gabaglio, V.: GPS/INS Integration for Pedestrian Navigation. Astronomisch-geodätische Arbeiten in der Schweiz, vol. 64 (2003) ISBN 3-908440-07-6 Google Scholar
  22. 22.
    Jirawimut, R., Ptasinski, P., Garaj, V., Cecelja, F., Balachandran, W.: A Method for Dead Reckoning Parameter Correction in Pedestrian Navigation system. Instrumentation and Measurement 52(1), 209–215 (2003)CrossRefGoogle Scholar
  23. 23.
    Kourogi, M., Kurata, T.: Personal Positioning based on Walking Locomotion Analysis with Self-Contained Sensors and a Wearable Camera. In: ISMAR 2003 (2003)Google Scholar
  24. 24.
    Beauregard, S., Haas, H.: Pedestrian Dead Reckoning: A Basis for Personal Positioning. In: WPNC 2006 (2006)Google Scholar
  25. 25.
    Bertram, J.E.A., Ruina, A.: Multiple Walking Speed-Frequency Relations are Predicted by Constrained Optimization. Journal of Theoretical Biology 209(4) (2001)Google Scholar
  26. 26.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. IJCV 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  27. 27.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. PAMI 27(10), 1615–1630 (2005)Google Scholar
  28. 28.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)CrossRefGoogle Scholar
  29. 29.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  30. 30.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  31. 31.
    Nister, D.: An efficient solution to the five-point relative pose problem. IEEE PAMI 26(6), 756–777 (2004)Google Scholar
  32. 32.
    Tuytelaars, T., Van Gool, L.: Content-based image retrieval based on local affinely invariant regions. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 493–500. Springer, Heidelberg (1999)Google Scholar
  33. 33.
    Hightower, J., Borriello, G.: Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ulrich Steinhoff
    • 1
  • Dušan Omerčević
    • 2
  • Roland Perko
    • 2
  • Bernt Schiele
    • 1
  • Aleš Leonardis
    • 2
  1. 1.TU DarmstadtGermany
  2. 2.University of LjubljanaSlovenia

Personalised recommendations