Optimal Proposal Distribution FastSLAM with Suitable Gaussian Weighted Integral Solutions

  • Qingling Li
  • Yu Song
  • ZengGuang Hou
  • Bin Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


One of the key issues in Gaussian SLAM is to calculate nonlinear transition density of Gaussian prior, i.e. to calculate Gaussian Weight Integral (GWI) whose integrand is with the form nonlinear function × Gaussian prior density. Up to now, some GWI solutions have been applied in SLAM (e.g. linearization, unscented transform and cubature rule), and different SLAM algorithms were derived based on theirs GWI solutions. While, how to select suitable GWI solution for SLAM is still lack of theoretical analysis. In this paper, we proposed an optimal proposal FastSLAM algorithm with suitable GWI solutions. The main contributions of this work lies that: (1) an unified FastSLAM framework with optimal proposal distribution is summarized; (2) a SLAM dimensionality based GWI solution selection criterion is designed; (3) we propose a new SLAM algorithm. The performance of the proposed SLAM is investigated and compared with the FastSLAM2.0 and UFastSLAM using simulations and our opinion is confirmed by the results.


Mobile Robot SLAM Unscented Transform Cubature Rule 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Doucet, A., Freitas, N.D.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 176–183 (2000)Google Scholar
  2. 2.
    Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: IEEE International Conference on Robotics and Automation, ICRA, pp. 1985–1991 (2003)Google Scholar
  3. 3.
    Montemerlo, M., Thrun, S., Koller, D., et al.: FastSLAM2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1151–1156 (2003)Google Scholar
  4. 4.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Transactions on Robotics (TRO) 23(1), 34–46 (2007)CrossRefGoogle Scholar
  5. 5.
    Sim, R., Elinas, P., Little, J.: A study of the Rao–Blackwellised particle filter for efficient and accurate vision-based SLAM. International Journal on Computer Vision (IJCV) 74(3), 303–318 (2007)CrossRefGoogle Scholar
  6. 6.
    Kim, C., Sakthivel, R., Chung, W.K.: Unscented FastSLAM: a robust and efficient solution to SLAM problem. IEEE Transactions on Robotics (TRO) 24(4), 808–820 (2008)CrossRefGoogle Scholar
  7. 7.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  8. 8.
    Julier, S.J.: A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control 45(3), 477–482 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lee, J.S., Kim, C.: Robust RBPF-SLAM using sonar sensors in non-static environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 250–256 (2010)Google Scholar
  10. 10.
    Moreno, L., Garrido, S., Blanco, D.: Bridging the gap between feature-and grid-based SLAM. Robotics and Autonomous Systems (RAS) 58, 140–148 (2010)CrossRefGoogle Scholar
  11. 11.
    Song, Y., Li, Q.L., Kang, Y.F., Song, Y.D.: CFastSLAM: a new Jacobian free solution to SLAM problem. In: IEEE International Conference on Robotics and Automation (2012)Google Scholar
  12. 12.
    Arasaratnam, I., Haykin, S.: Cubature kalman filters. IEEE Transaction on Automatic Control (TAC) 54(6), 1254–1269 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qingling Li
    • 1
  • Yu Song
    • 2
  • ZengGuang Hou
    • 3
  • Bin Zhu
    • 1
  1. 1.Department of Mechanical EngineeringChina University of Mining & TechnologyBeijingChina
  2. 2.School of Electronic & Information EngineeringBeijing Jiaotong UniversityBeijingChina
  3. 3.Institute of Automation, Chinese Academy of ScienceBeijingChina

Personalised recommendations