Skip to main content
SpringerLink
Log in

GPS/odometry/map fusion for vehicle positioning using potential function

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper, we present a fusion approach to localize urban vehicles by integrating a visual odometry, a low-cost GPS, and a two-dimensional digital road map. Distinguished from conventional sensor fusion methods, two types of potential functions (i.e. potential wells and potential trenches) are proposed to represent measurements and constraints, respectively. By choosing different potential functions according to data properties, data from various sensors can be integrated with intuitive understanding, while no extra map matching is required. The minimum of fused potential, which is regarded as position estimation, is confined such that fast minimum searching can be achieved. Experiments under realistic conditions have been conducted to validate the satisfactory positioning accuracy and robustness compared to pure visual odometry and map matching methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alonso, I. P., Llorca, D. F., Gavilán, M., Pardo, S. Á., García-Garrido, M. Á., Vlacic, L., et al. (2012). Accurate global localization using visual odometry and digital maps on urban environments. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1535–1545.

    Article  Google Scholar 

  • Barshan, B., & Durrant-Whyte, H. F. (1995). Inertial navigation systems for mobile robots. IEEE Transactions on Robotics and Automation, 11(3), 328–342.

    Article  Google Scholar 

  • Blanco-Claraco, J.-L., Moreno-Dueñas, F.-Á., & González-Jiménez, J. (2014). The málaga urban dataset: High-rate stereo and lidar in a realistic urban scenario. The International Journal of Robotics Research, 33(2), 207–214.

    Article  Google Scholar 

  • Borenstein, J., & Feng, L. (1996). Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 12(6), 869–880.

    Article  Google Scholar 

  • Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Choset, H. M. (2005). Principles of robot motion: Theory, algorithms, and implementation. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Crassidis, J. L. (2006). Sigma-point kalman filtering for integrated gps and inertial navigation. IEEE Transactions on Aerospace and Electronic Systems, 42(2), 750–756.

    Article  Google Scholar 

  • El Najjar, M. E., & Bonnifait, P. (2005). A road-matching method for precise vehicle localization using belief theory and kalman filtering. Autonomous Robots, 19(2), 173–191.

    Article  Google Scholar 

  • Ge, S. S., & Fua, C.-H. (2005). Queues and artificial potential trenches for multirobot formations. IEEE Transactions on Robotics, 21(4), 646–656.

    Article  Google Scholar 

  • Geiger, A., Ziegler, J., & Stiller, C. (2011). Stereoscan: Dense 3d reconstruction in real-time. In Intelligent Vehicles Symposium (IV), 2011 IEEE (pp. 963–968). IEEE

  • Hall, D., & Llinas, J. (2001). Multisensor data fusion. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Huang, P., & Pi, Y. (2011). Urban environment solutions to gps signal near-far effect. IEEE Aerospace and Electronic Systems Magazine, 26(5), 18–27.

    Article  Google Scholar 

  • Jagadeesh, G. R., Srikanthan, T., & Zhang, X. D. (2004). A map matching method for gps based real-time vehicle location. Journal of Navigation, 57(03), 429–440.

    Article  Google Scholar 

  • Jiang, R., Klette, R., & Wang, S. (2010). Modeling of unbounded long-range drift in visual odometry. In Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium on (pp. 121–126). IEEE.

  • Jiang, R., Yang, S., Ge, S. S., Wang, H., & Lee, T. H. (2017). Geometric map-assisted localization for mobile robots based on uniform-Gaussian distribution. IEEE Robotics and Automation Letters, 2(2), 789–795.

    Article  Google Scholar 

  • Jo, K., Chu, K., & Sunwoo, M. (2012). Interacting multiple model filter-based sensor fusion of gps with in-vehicle sensors for real-time vehicle positioning. IEEE Transactions on Intelligent Transportation Systems, 13(1), 329–343.

    Article  Google Scholar 

  • Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90–98.

    Article  Google Scholar 

  • Kong, S.-H. (2011). Statistical analysis of urban gps multipaths and pseudo-range measurement errors. IEEE Transactions on Aerospace and Electronic Systems, 47(2), 1101–1113.

    Article  Google Scholar 

  • Noureldin, A., Karamat, T. B., Eberts, M. D., & El-Shafie, A. (2009). Performance enhancement of mems-based ins/gps integration for low-cost navigation applications. IEEE Transactions on Vehicular Technology, 58(3), 1077–1096.

    Article  Google Scholar 

  • Ramisa, A., Tapus, A., Aldavert, D., Toledo, R., & de Mantaras, R. L. (2009). Robust vision-based robot localization using combinations of local feature region detectors. Autonomous Robots, 27(4), 373–385.

    Article  Google Scholar 

  • Scaramuzza, D., & Fraundorfer, F. (2011). Visual odometry [tutorial]. IEEE Robotics & Automation Magazine, 18(4), 80–92.

    Article  Google Scholar 

  • Se, S., Lowe, D. G., & Little, J. J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 21(3), 364–375.

    Article  Google Scholar 

  • Smith, D., & Singh, S. (2006). Approaches to multisensor data fusion in target tracking: A survey. IEEE Transactions on Knowledge and Data Engineering, 18(12), 1696–1710.

    Article  Google Scholar 

  • Wei, L., Cappelle, C., & Ruichek, Y. (2013). Camera/laser/GPS fusion method for vehicle positioning under extended nis-based sensor validation. IEEE Transactions on Instrumentation and Measurement, 62(11), 3110–3122.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Defence Innovative Research Programme (DIRP).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore

    Rui Jiang, Shuzhi Sam Ge, Xiaomei Liu & Tong Heng Lee

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Shuai Yang & Han Wang

Authors
  1. Rui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuzhi Sam Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaomei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tong Heng Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuzhi Sam Ge.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, R., Yang, S., Ge, S.S. et al. GPS/odometry/map fusion for vehicle positioning using potential function. Auton Robot 42, 99–110 (2018). https://doi.org/10.1007/s10514-017-9646-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-017-9646-9

Keywords

Search

Navigation