Journal of Intelligent & Robotic Systems

, Volume 78, Issue 2, pp 185–204 | Cite as

Airborne Vision-Aided Navigation Using Road Intersection Features

  • Steven J. DumbleEmail author
  • Peter W. Gibbens


Modern airborne navigation systems for manned and unmanned platforms usually rely on GPS measurements to constrain the inertial position estimate of the platform. This reliance on GPS can quickly cause the navigational estimates of system to become unreliable when the system is operating in GPS-limited or denied areas. This paper presents a vision-aided inertial navigation system that uses ground features (in this case road intersections) matched to a database to provide position measurements. An image processing algorithm is used to extract the shapes of the road intersections from visual imagery, this shape is then matched to a reference database to provide image to map road intersection correspondences. The correspondence information is fused with the inertial solution in an Extended Kalman Filter to constrain the complete attitude and position inertial navigation solution. The system is developed to operate at non-zero attitude angles removing the level flight limitations of past approaches. Flight test results of the system demonstrate that the system can successfully produce accurate navigation estimates that are comparable to the use of GPS without the same limitations of GPS when operating in a GPS-limited or denied area.


Vision systems Inertial navigation Image processing Visual navigation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

(AVI 52.0 MB)


  1. 1.
    Ali, H.M., Boshir, A., Ariful, I.M.: Automatic extractions of road intersections from satellite imagery in urban areas. In: International Conference on Electrical and Computer Engineering, pp. 686–689 (2010)Google Scholar
  2. 2.
    Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): part II. IEEE Robot. Autom. Mag. 13, 108–117 (2006)CrossRefGoogle Scholar
  3. 3.
    Bekir, E.: Introduction to modern navigation systems: World scientific (2007)Google Scholar
  4. 4.
    Bryson, M., Sukkarieh, S.: Bearing-only SLAM for an airborne vehicle. In: Australasian Conference Robotic and Automation (2005)Google Scholar
  5. 5.
    Bryson, M., Sukkarieh, S.: Building a robust implementation of bearing-only inertial SLAM for a UAV. J. Field Robot. 24, 113–143 (2007)CrossRefGoogle Scholar
  6. 6.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-based Odometry and SLAM for medium and high altitude flying UAVs. J. Intell. Robot. Syst. 54, 137–161 (2009)CrossRefGoogle Scholar
  7. 7.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Unmanned Aerial vehicle localization based on monocular vision and online mosaicking. J. Intell. Robot. Syst. 55, 323–343 (2009)CrossRefzbMATHGoogle Scholar
  8. 8.
    Chen, Z., Samarabandu, J., Rodrigo, R.: Recent advances in simultaneous localization and map-building using computer vision. Adv. Robot. 3, 233–265 (2007)CrossRefGoogle Scholar
  9. 9.
    Conte, G., Doherty, P.: An Integrated UAV navigation system based on aerial image matching. In: IEEE Aerospace Conference, pp. 1–10 (2008)Google Scholar
  10. 10.
    Conte, G., Doherty, P.: Use of Geo-referenced images with unmanned aerial systems. In: International Conference on Simulation Modeling and Programming for Autonomous Robots, pp. 444–454 (2008)Google Scholar
  11. 11.
    Conte, G., Doherty, P.: Vision-based unmanned aerial vehicle navigation using Geo-referenced information. J. Adv. Sig. Process. 2009, 10:1–10:18 (2009)Google Scholar
  12. 12.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13, 99–110 (2006)CrossRefGoogle Scholar
  13. 13.
    Farrell, J.: Aided navigation. McGraw-Hill, GPS with High Rate Sensors (2008)Google Scholar
  14. 14.
    Fischler, M.A., Bolles, R. C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  15. 15.
    George, M., Sukkarieh, S.: Inertial navigation aided by monocular camera observations of unknown features. In: IEEE International Conference on Robotics and Automation, pp. 3558–3564 (2007)Google Scholar
  16. 16.
    Grewal, M.S., Weill, L.R., Andrews, A.P.: Global positioning systems, Inertial Navigation, and Integration. Wiley (2007)Google Scholar
  17. 17.
    Groves, P.D.: Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems. Artech House (2008)Google Scholar
  18. 18.
    Gu, D.-Y., Zhu, C.-F., Guo, J., Li, S.-X., Chang, H.-X.: Vision-aided UAV navigation using GIS data. In: IEEE International Conference on Vehicular Electronics and Safety, pp. 78–82 (2010)Google Scholar
  19. 19.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)Google Scholar
  20. 20.
    Hu, J., Razdan, A., Femiani, J.C., Cui, M., Wonka, P.: Road network extraction and intersection detection from aerial images by tracking road footprints. In: IEEE Transactions on Geoscience and Remote Sensing, vol. 45, pp. 4144–4157 (2007)Google Scholar
  21. 21.
    Imagery, N., Agency, M.: Department of defense world geodetic system 1984: its definition and relationships with local geodetic systems. In: National Imagery and Mapping Agency (2000)Google Scholar
  22. 22.
    Jung, J., Yun, J., Ryoo, C.-K., Choi, K.: Vision based navigation using road-intersection image. In: 11th International Conference on Control Automation and Systems, pp. 964–968 (2011)Google Scholar
  23. 23.
    Kayton, M., Fried, W.R.: Avionics navigation systems. Wiley (1997)Google Scholar
  24. 24.
    Kim, J., Sukkarieh, S.: Autonomous airborne navigation in unknown terrain environments. IEEE Trans. Aerosp. Electron. Syst. 40, 1031–1045 (2004)CrossRefGoogle Scholar
  25. 25.
    Kim, J., Sukkarieh, S.: SLAM aided GPS/INS navigation in GPS denied and unknown environments. In: International Symposium on GNSS/GPS (2004)Google Scholar
  26. 26.
    Kim, J., Sukkarieh, S.: 6DoF SLAM aided GNSS/INS navigation in GNSS denied and unknown environments. J. Glob. Positioning Syst. 4, 120–128 (2005)CrossRefGoogle Scholar
  27. 27.
    Kim, J., Sukkarieh, S.: Real-time implementation of airborne inertial-SLAM. Robot. Auton. Syst. 55, 62–71 (2007)CrossRefGoogle Scholar
  28. 28.
    Lee, M.G., Park, J.H., Pan, S.B., Kim, R.C., Kim, K.S., Park, R.H., et al.: Implementation of the image-based absolute position compensation algorithm in the navigation parameter extraction system. In: Internation Conference of Signal Processing Applications Technology, pp. 1603–1607 (1998)Google Scholar
  29. 29.
    Li, Y., Briggs, R.: Automatic extraction of roads from high resolution aerial and satellite images with heavy noise. Optimization, 416–422 (2009)Google Scholar
  30. 30.
    Liang, W., Yunan, H.: Vision-aided navigation for aircrafts based on road junction detection. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 164–169 (2009)Google Scholar
  31. 31.
    Lindsten, F., Callmer, J., Ohlsson, H., Tornqvist, D., Schon, T. B., Gustafsson, F.: Geo-referencing for UAV navigation using environmental classification. In: IEEE International Conference on Robotics and Automation, pp. 1420–1425 (2010)Google Scholar
  32. 32.
    Mena, J. B.: Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images. In: Knowledge-Based Systems, vol. 19, pp. 704–718 (2006)Google Scholar
  33. 33.
    Merino, L., Wiklund, J., Caballero, F., Moe, A., De Dios, J. R. M., Forssen, P.-E., et al.: Vision-based multi-UAV position estimation. IEEE Robot. Autom. Mag. 13, 53–62 (2006)CrossRefGoogle Scholar
  34. 34.
    Merino, L., Caballero, F., Forssen, P., Wiklund, J., Ferruz, J., Martihez-de-Dios, J. R., et al.: Single and multi-UAV relative position estimation based on natural landmarks. In: Advances in Unmanned Aerial Vehicles. vol. 33, pp. 267–307. Springer, Netherlands (2007)Google Scholar
  35. 35.
    Patterson, T., McClean, S., Morrow, P., Parr, G.: Utilizing geographic information system data for unmanned aerial vehicle position estimation. In: Canadian Conference on Computer and Robot Vision, pp. 8–15 (2011)Google Scholar
  36. 36.
    Ravanbakhsh, M., Heipke, C., Pakzad, K.: Road junction extraction from high-resolution aerial imagery. Photogramm. Rec. 23, 405–423 (2008)CrossRefGoogle Scholar
  37. 37.
    Sim, D.-G., Jeong, S.-Y., Park, R.-H., Kim, R.-C., Lee, S. U., Kim, I. C.: Navigation parameter estimation from sequential aerial images. In: International Conference on Image Processing, pp. 629–632 (1996)Google Scholar
  38. 38.
    Sim, D.-G., Park, R.-H., Kim, R.-C., Lee, S.-U., Kim, I.-C.: Integrated position estimation using aerial image sequences. IEEE Trans. Pattern Anal. Mach. Intel. 24, 1–18 (2002)CrossRefGoogle Scholar
  39. 39.
    Song,M., Civco, D.: Road extraction using SVM and image segmentation. Photogramm. Eng. Remote Sens. 70, 1365–1371 (2004)Google Scholar
  40. 40.
    Titterton, D.H., Weston, J.L.: Strapdown inertial navigation technology, 2nd edn. The Institution of Engineering and Technology (2004)Google Scholar
  41. 41.
    Trawny, N., Mourikis, A.I., Roumeliotis, S.I., Johnson, A.E., Montgomery, J. F.: Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks. J. Field Robot. 24, 357–378 (2007)CrossRefGoogle Scholar
  42. 42.
    Zhu, C.-F., Li, S.-X., Chang, H.-X., Zhang, J.-X.: Matching road networks extracted from aerial images to GIS data. In: Asia-Pacific Conference on Information Processing, pp. 63–66 (2009)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.University of SydneySydneyAustralia

Personalised recommendations