Skip to main content
SpringerLink
Log in

On the use of likelihood fields to perform sonar scan matching localization

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Scan matching algorithms have been extensively used in the last years to perform mobile robot localization. Although these algorithms require dense and accurate sets of readings with which to work, such as the ones provided by laser range finders, different studies have shown that scan matching localization is also possible with sonar sensors. Both sonar and laser scan matching algorithms are usually based on the ideas introduced in the ICP (Iterative Closest Point) approach. In this paper a different approach to scan matching, the Likelihood Field based approach, is presented. Three scan matching algorithms based on this concept, the non filtered sNDT (sonar Normal Distributions Transform), the filtered sNDT and the LF/SoG (Likelihood Field/Sum of Gaussians), are introduced and analyzed. These algorithms are experimentally evaluated and compared to previously existing ICP-based algorithms. The obtained results suggest that the Likelihood Field based approach compares favorably with algorithms from the ICP family in terms of robustness and accuracy. The convergence speed, as well as the time requirements, are also experimentally evaluated and discussed.

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

  • Barshan, B. (2007). Directional processing of ultrasonic arc maps and its comparison with existing techniques. International Journal of Robotics Research, 26(8), 797–820.

    Article  Google Scholar 

  • Besl, P., & McKay, N. (1992). A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256.

    Article  Google Scholar 

  • Biber, P. (2007). Map building and localization for long-term operation of mobile robots in dynamic environments. PhD thesis, Wilhelm-Schickard-Institut, University of Tuebingen, Germany.

  • Biber, P., & Straßer, W. (2003). The normal distribution transform: a new approach to laser scan matching. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 3, pp. 2743–2748).

  • Biber, P., Fleck, S., & Straßer, W. (2004). A probabilistic framework for robust and accurate matching of point clouds. In Proceedings of the 26th pattern recognition symposium (DAGM).

  • Bosse, M., Newman, P. M. Leonard, J.J. & Teller, S. (2004). SLAM in large-scale cyclic environments using the atlas framework. International Journal of Robotics Research, 23(12), 1113–1139.

    Google Scholar 

  • Burguera, A., Oliver, G., & Tardós, J. D. (2005). Robust scan matching localization using ultrasonic range finders. In Proceedings of the conference on intelligent robots and systems (IROS) (pp. 1451–1456), Edmonton, Canada.

  • Burguera, A., González, Y., & Oliver, G. (2007a), Probabilistic filtering of sonar data. In IFAC symposium on intelligent autonomous vehicles (IAV), Toulouse, France.

  • Burguera, A., González, Y., & Oliver, G. (2007b). Probabilistic sonar filtering in scan matching localization. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS). San Diego, USA.

  • Burguera, A., González, Y., & Oliver, G. (2007c). Probabilistic sonar scan matching for robust localization. In Proceedings of the international conference on robotics and automation (ICRA), Rome, Italy.

  • Burguera, A., González, Y., & Oliver, G. (2008). A probabilistic framework for sonar scan matching localization. Advanced Robotics, 22(11), 1223–1241.

    Article  Google Scholar 

  • Castellanos, J. A., Neira, J., & Tardós, J. D. (2001). Multisensor fusion for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 17(6), 908–914.

    Article  Google Scholar 

  • Chen, Y., & Medioni, G. (1992). Object modelling by registration of multiple range images. International Journal of Computer Vision and Image Understanding, 10(3), 145–155.

    Google Scholar 

  • Dissanayake, G., Williams, S. B., Durrant-Whyte, H., & Bailey, T. (2002). Map management for efficient simultaneous localization and mapping (SLAM). Autonomous Robots, (12), 267–286.

    Article  MATH  Google Scholar 

  • Elfes, A. (1989). Occupancy grids: a probabilistic framework for mobile robot perception and navigation. PhD thesis, Dept. Electrical and Computer Engineering. Robotics Institute, Carnegie Mellon University.

  • Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.

    Article  MathSciNet  Google Scholar 

  • Gutmann, J. S., & Schlegel, C. (1996). AMOS: comparison of scan matching approaches for self-localization in indoor environments. In Proceedings of the Euromicro workshop in advanced mobile robotics.

  • Hähnel, D., Burgard, W., & Thrun, S. (2003). Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems, 44(1), 15–27.

    Article  Google Scholar 

  • Lee, D. (1996). The map-building and exploration strategies of a simple sonar-equipped mobile robot. Cambridge: Cambridge University Press.

    Google Scholar 

  • Leonard, J., & Durrant-Whyte, H. F. (1992). Directed sonar sensing for mobile robot navigation. Cambridge: Kluwer Academic.

    MATH  Google Scholar 

  • Lu, F., & Milios, E. (1997). Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems, 18(3), 249–275.

    Article  Google Scholar 

  • Minguez, J., Montesano, L., & Lamiraux, F. (2006). Metric-based iterative closest point scan matching for sensor displacement estimation. IEEE Transactions on Robotics, 22(5), 1047–1054.

    Article  Google Scholar 

  • Montesano, L., Minguez, J., & Montano, L. (2005). Probabilistic scan matching for motion estimation in unstructured environments. In Proceedings of the conference on intelligent robots and systems (IROS) (pp. 1445–1450).

  • Moravec, H. P. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9, 61–74.

    Google Scholar 

  • Pfister, S. T., Kreichbaum, K. L., Roumeliotis, S. I., & Burdick, J. W. (2002). Weighted range sensor matching algorithms for mobile robot displacement estimation. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (Vol. 2, pp. 1667–1674).

  • Tardós, J. D., Neira, J., Newman, P. M., & Leonard, J. J. (2002). Robust mapping and localization in indoor environments using sonar data. International Journal of Robotics Research, 21(4), 311–330.

    Article  Google Scholar 

  • Thrun, S. (2001). A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5), 335–363.

    Article  Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Weiss, G., & Puttkamer, E. V. (1995). A map based on laserscans without geometric interpretation. In Proceedings of intelligent autonomous systems (IAS) (Vol. 4, pp. 403–407).

  • Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision, 13(2), 119–152.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Matemàtiques i Informàtica, Universitat de les Illes Balears, Ctra. Valldemossa, Km. 7’5, 07122, Palma de Mallorca, Illes Balears, Spain

    Antoni Burguera, Yolanda González & Gabriel Oliver

Authors
  1. Antoni Burguera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yolanda González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel Oliver
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antoni Burguera.

Additional information

This work is partially supported by DPI 2005-09001-C03-02 and FEDER funding.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Burguera, A., González, Y. & Oliver, G. On the use of likelihood fields to perform sonar scan matching localization. Auton Robot 26, 203–222 (2009). https://doi.org/10.1007/s10514-009-9108-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-009-9108-0

Keywords

Search

Navigation