Skip to main content
Log in

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

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


We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others


  1. Rekleitis, I., Meger, D., Dudek, G.: Simultaneous planning, localization, and mapping in a camera sensor network. Robot. Auton. Syst. 54, (8), 921–932 (2006)

    Article  Google Scholar 

  2. Punithakumar, K., kirubarajan, T., Sinha, A.: A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks. In: Proceedings of the Signal Processing, Sensor Fusion, and Target Recognition XV, Kissimmee, FL (2006)

  3. Vemula, M., Djuric, P.M.: Multisensor fusion for target tracking using sequential Monte Carlo methods. In: Proceedings of the IEEE/SP 13th Workshop on Statistical Signal Processing. Bordeaux, France, pp. 1223–1230 (2006)

  4. Xiaohong S., Hen H.Y.: Sequential acoustic energy based source localization using particle filter in a distributed sensor network. In: Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Que., Canada, pp. 972–975 (2004)

  5. Guo, D., Wang, X.: Dynamic sensor collaboration via sequential Monte Carlo. IEEE J. Sel. Areas Commun. 22, (8), 1037–1047 (2004)

    Article  Google Scholar 

  6. Wang, H., Yip, L., Yao, K., et al: Lower bounds of localization uncertainty in sensor networks. In: Proceedings of the ICASSP, Montreal, Canada (2004)

  7. Wang H., Yao K., Pottie G., et al: Entropy-based sensor selection heuristic for target localization. In: Proceedings of the IPSN’04, Berkeley, CA (2004)

  8. Kumar, S., Shepherd, D., Zhao, F.: Collaborative signal and information processing in microsensor networks. IEEE Signal Process. Mag. 19, (2), 13–14 (2002)

    Article  Google Scholar 

  9. Zhao, F., Shin, J., Reich, J.: Information-driven dynamic sensor collaboration. IEEE Signal Process. Mag. 19, (5), 61–72 (2002)

    Article  Google Scholar 

  10. Dailey, M.N., Parnichkun, M.: Simultaneous localization and mapping with stereo vision. In: Proceedings of the 9th international Conference on Control, Automation, Robotics and Vision, Singapore (2006)

  11. Li, M.H., Hong, B.R.: Novel method of mobile simultaneous localization and mapping. J. Nanjing Univ. Sci. Technol. 30, (3), 302–310 (2006)

    MathSciNet  Google Scholar 

  12. Kwok, C., Fox, D., Meila, M.: Real-time particle filters. Proc. IEEE 92, 469–484 (2004)

    Article  Google Scholar 

  13. Linaker, F., Ishikawa, M.: Rotation invariant features from omnidirectional camera images using a polar higher-order local autocorrelation feature extractor. In: Proceedings of the IEEE/RSJ Interational Conference on Intelligent Robots and Systems (IROS), Sendai, Japan, pp. 4026–4031 (2004)

  14. Liu, J.S., Chen, R.: A theoretical framework for sequential importance sampling and resampling. In: Smith, A., Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo in Practice. Springer, New York (2001)

    Google Scholar 

  15. Thrun, S., Langford, J.: Monte Carlo hidden Markov models, TR CMU-CS-98-179. Carnegie Mellon University (1997)

  16. Feng, L., Borenstein, J., Everett, H.R.: Where am I: sensors and methods for mobile robot positioning, Technical Report UM-MEAM-94-21. University of Michigan (1994)

  17. Rekleitis, I. A particle filter tutorial for mobile robot localization. Technical Report. TR-CIM-04-04, McGill University (2004)

  18. Thrun, S., Fox, D., Burgard, W.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128, 99–141 (2001)

    Article  MATH  Google Scholar 

  19. Tsai, Roger Y.: An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 364–374. Miami Beach, FL (1986)

  20. Howard, A., Mataric, M.J.: Localization for mobile robot teams using maximum likelihood estimation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. EPFL Switzerland, pp.434–459 (2002)

  21. Dellaert, F., Fox, D., Burgard, W., et al: Monte Carlo localization for mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (1999)

  22. Thrun, S., Langford, J.C., and Fox, D.: Monte Carlo hidden Markov models: learning non-parametric models of partially observable stochastic processes. In: Proceedings of the 16th International Conference on Machine Learning. pp. 415–424 (1999)

  23. Moore, A.W., Schneider, J., Deng, K.: Efficient locally weighted polynomial regression predictions. ICML-97 (1997)

  24. Wu ,Q.X., Bell, D.A., Guo, G.D.: New solutions for kidnapped robot problem in Markov localization algorithm. In: Proceedings of the International Conference on Engineering of Intelligent Systems (EIS), Canada/The Netherlands (2002)

  25. Liu, J.S., Chen, R.: Sequential Monte Carlo methods for dynamic systems. J. Am. Stat. Assoc. 93, 1032–1044 (1998)

    Article  MATH  Google Scholar 

  26. Simmons, R.: The inter-process communication (IPC) system, (2005)

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Zhiwei Liang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liang, Z., Ma, X. & Dai, X. Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization. J Intell Robot Syst 52, 157–174 (2008).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: