Skip to main content
SpringerLink
Log in

Vision system as an aid to car navigation

  • Published:
Gyroscopy and Navigation Aims and scope Submit manuscript

Abstract

We focus on car navigation method using vision system and digital road map. Vision system installed onboard the car serves as an additional source of navigation information. It is used to determine the car lateral deviation relative to the road axial line, range and bearing to the intersection center. Algorithm generating these parameters is described with account for the topology and parameters of roads presented in the digital map. Accuracy of lateral deviation and range measurements is estimated using real data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Oh, S.M., Tariq, S., Walker, B., and Dellaert, F., Mapbased priors for localization, IROS, 2004, p.1–6.

    Google Scholar 

  2. Google Self-Driving Car Project/ Main page, http://www.google.com/selfdrivingcar/.

  3. Velodyne lidar / Main page, http://www.velodynelidar. com.

  4. Dmitriev, S.P., Stepanov, O.A., Rivkin, B.S., Koshaev, D.A., and Chang, D., Optimal solution to car navigation problem using road map, Giroskopiya i navigatsiya, 2000, no. 2(29), pp.57.

    Google Scholar 

  5. Ignacio, P. and Miguel-Angel, S., Visual odometry and map fusion for GPS navigation assistance, IEEE International Symposium on Industrial Electronics, 2011, p. 832–837.

    Google Scholar 

  6. Toropov, A.B. and Stepanov, O.A., Nonlinear filtering for map-aided navigation. Part 1. An overview of algorithms, Gyroscopy and Navigation, 2015, no. 4, pp. 324–338.

    Google Scholar 

  7. Toropov, A.B. and Stepanov, O.A., Nonlinear filtering for map-aided navigation. Part 2. Trends in the algorithm development, Gyroscopy and Navigation, 2016, no. 1, pp. 82–90.

    Google Scholar 

  8. Berkovitch, S.B., Kotov, N.I., Sholokhov, A.V., Sadekov, R.N. et al., Using digital road maps for autonomous correction of ground navigation systems, Trudy FGUP NPTs AP, Sistemy i pribory upravleniya, 2008, no. 2, pp. 87–95.

    Google Scholar 

  9. Davidson, P., Application of particle filters to mapmatching algorithm, 17th International Conference on Integrated Navigation Systems, St. Petersburg, Elektropribor, Russia, 2010, p. 303–308.

    Google Scholar 

  10. Brubaker, M.A., Geiger, A., and Urtasun, R., Mapbased probabilistic visual self-localization, PAMI, 2016 IEEE Conference, 2016.

    Google Scholar 

  11. Stockman, G. and Shapiro, L.G., Computer Vision. Prentice Hall, 2001, 609 p.

    Google Scholar 

  12. Forsyth, D.A. and Ponce, J., Computer Vision: A Modern Approach. Prentice Hall, 2011, 792 p.

    Google Scholar 

  13. Sadekov, R.N., Using computer vision methods for navigation of cars and ground vehicles, Giroskopiya i navigatsiya, 2015, no. 2, pp. 58–66.

    Google Scholar 

  14. Shiho, T., Kenichi, Y., Toshio, I., and Takenao, O., Vehicle detection based on perspective transformation using rear-view camera, International journal of vehicular technology, 2011, Article ID279739, p. 9. doi 10.1155/2011/279739

    Google Scholar 

  15. Chien-Chuan Lin and Ming-Shi Wang, A vision based top-view transformation model for a vehicle parking assistant, 2012, pp. 1–16. doi 10.3390/s120404431

    Google Scholar 

  16. Barber, D., Bayesian Reasoning and Machine Learning, 2015.

    MATH  Google Scholar 

  17. Stepanov, O.A., Osnovy teorii otsenivaniya s prilozheniyami k zadacham obrabotki navigatsionnoi informatsii. Part 1. Vvedenie v teoriyu otsenivaniya (Fundamentals of the Estimation Theory with Applications to the Problems of Navigation Information Processing. Part 1. Introduction to the Estimation Theory), St. Petersburg: CSRI Elektropribor, 2009.

    Google Scholar 

  18. OpenCV / Main page http://www.opencv.org.

  19. Fritsch, J., Kuehl, T., and Geiger, A., A new performance measure and evaluation benchmark for road detection algorithms, PAMI, 2013 IEEE Conference.

    Google Scholar 

  20. Open Street map / Main page http://www.openstreetmap.org.

Download references

Author information

Authors and Affiliations

  1. Institute of Engineering Physics, Serpukhov, Russia

    S. B. Berkovich, N. I. Kotov, A. S. Lychagov, R. N. Sadekov & A. V. Sholokhov

  2. National University of Science and Technology MISiS, Moscow, Russia

    N. V. Panokin

Authors
  1. S. B. Berkovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. I. Kotov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. S. Lychagov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. V. Panokin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. N. Sadekov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. V. Sholokhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. N. Sadekov.

Additional information

Original Russian Text © S.B. Berkovich, N.I. Kotov, A.S. Lychagov, N.V. Panokin, R.N. Sadekov, A.V. Sholokhov, 2017, published in Giroskopiya i Navigatsiya, 2017, No. 1, pp. 49–63.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berkovich, S.B., Kotov, N.I., Lychagov, A.S. et al. Vision system as an aid to car navigation. Gyroscopy Navig. 8, 200–208 (2017). https://doi.org/10.1134/S2075108717030026

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2075108717030026

Search

Navigation