Skip to main content
SpringerLink
Log in

Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.

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. Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  2. Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Lemmer, J.F., Kanal, L.N. (eds.) Uncertainty in Artificial Intelligence 2. Elsevier, New York (1988)

    Google Scholar 

  3. Liu, Y., Thrun, S.: Results for outdoor-SLAM using sparse extended information filters. IEEE Int. Conf. Robot. Autom. 1, 1227–1233 (2003)

    Google Scholar 

  4. Guivant, J., Nebot, E.: Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans. Robot. Autom. 17(3), 242–257 (2001). doi:10.1109/70.938382

    Article  Google Scholar 

  5. Williams, S., Dissanayake, G., Durrant-Whyte, H.F.: An efficient approach to the simultaneous localization and mapping problem. IEEE Int. Conf. Robot. Autom. 15(5), 406–411 (2002)

    Google Scholar 

  6. Tardos, J.D., Neira, J., Newman, P., Leonard, J.J.: Robust mapping and localization in indoor environment using sonar data. Int. J. Rob. Res. 21(4), 311–330 (2002)

    Article  Google Scholar 

  7. Julier, S., Uhlmann, J.K.: A counter example to the theory of simultaneous localization and map building. IEEE ICRA’01. V.4. Seul, Corea., pp. 4238–4243 (2001)

  8. Castellanos, J.A., Neira, J., Tardos, J.D.: Limits to the consistency of EKF-based SLAM. In: Fifth IFAC Symposium on Intelligent Autonomous Vehicles IAV’04. Lisbon, Portugal (2004)

  9. Rodriguez-Losada, D., Matia, F., Pedraza, L., Jimenez, A., Galan, R.: Consistency of SLAM-EKF algorihtms for indoor environments. J. Intell. Robot. Syst. 50(4), 375–397 (2007). Springer. ISSN 0921-0296

    Article  MATH  Google Scholar 

  10. Doucet, A., de Freitas, J.F.G., Murphy, K., Russel, S.: Raoblackwellized particle filtering for dynamic bayesian networks. Conf. on Uncertainty in Artifcial Intelligence (UAI) (2000)

  11. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. AAAI Nat. Conf. on Artif. Intelligence. Edmonton, Canada (2002)

  12. Eliazar, A., Parr, R.: DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks. In: Proc. of the Int. Conf. on Artificial Intelligence (IJCAI) (2003)

  13. Grisetti, G., Stachniss, C., Burgard, W.: Improving Grid-based SLAM with rao-blackwellized particle filters by adaptive proposals and selective resampling. IEEE ICRA’ 05, pp. 2443–2448, Barcelona, Spain (2005)

  14. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. IJCAI, pp. 1151–1156 (2003)

  15. Hahnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. IEEE/RSJ IROS’03 (2003)

  16. Elinas, P., Sim, R., Little, J.J.: σSLAM: Stereo Vision SLAM using the rao-blackwellised particle filter and a novel mixture proposal distribution. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1564–1570. Orlando, FL, (2006)

  17. Bailey, T., Nieto, J., Nebot, E.: Consistency of the FastSLAM algorithm. In: IEEE International Conference on Robotics and Automation, 2006, pp. 424–429. Orlando, FL, USA, (2006)

  18. Castellanos, J.A., Montiel, J.M., Neira, J., Tardos, J.D.: The SPmap: a probabilistic framework for simultaneous localization and map building. IEEE Trans. Robot. Autom. 15(5), 948–953 (1999)

    Article  Google Scholar 

  19. Rodriguez-Losada, D., Matia, F., Galan, R.: Building geometric feature based maps for indoor service robots. Robot. Auton. Syst. 54, 546–558 (2006). doi:10.1016/j.robot.2006.04.003

    Article  Google Scholar 

  20. Montemerlo, M.: FastSLAM: a factored solution to the simultaneous localization and mapping problem with unknown data association. PhD doctoral dissertation, CMU-RI-TR-03-28, Robotics Institute, Carnegie Mellon University, July (2003)

  21. Neira, J., Tardos, J.D.: Data association in stochastic mapping using the joint compatibility test. IEEE Trans. Robot. Autom. 17(6), 890–897 (2001). doi:10.1109/70.976019

    Article  Google Scholar 

  22. Lacey, G., Rodriguez-Losada, D.: The evolution of Guido: a smart walker for the blind. Accepted IEEE Robot. Autom. Mag. 15(4), 75–83 (2008). doi:10.1109/MRA.2008.929924

    Article  Google Scholar 

  23. Rodriguez-Losada, D., Matia, F., Jiménez, A., Galan, R., Lacey, G.: Implementing map based navigation in Guido, the robotic SmartWalker. In: IEEE International Conference on Robotics and Automation, pp. 3401–3406. Barcelona, Spain, (2005)

  24. Bosse, M., Newman, P., Leonard, J., Teller, S.: Simultaneous localization and map building in large-scale cyclic environments using the atlas framework. Int. J. Robot. Res 23(12), 1113–1140 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Politecnica de Madrid, C/Jose Gutierrez Abascal 2, 28006, Madrid, Spain

    D. Rodriguez-Losada, P. San Segundo, F. Matia & L. Pedraza

Authors
  1. D. Rodriguez-Losada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. San Segundo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Matia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Pedraza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Rodriguez-Losada.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodriguez-Losada, D., San Segundo, P., Matia, F. et al. Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem. J Intell Robot Syst 55, 109–134 (2009). https://doi.org/10.1007/s10846-008-9296-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9296-4

Keywords

Search

Navigation