Skip to main content
SpringerLink
Log in

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighborhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue.

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

  • Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

    Article  Google Scholar 

  • Bar-Shalom, Y., & Li, X. R. (1995). Multitarget-multisensor tracking: principles and techniques. Storrs: YBS Publisher.

    Google Scholar 

  • Barker, A. L., Brown, D. E., & Martin, W. N. (1994). Bayesian estimation and the Kalman filter. Technical Report IPC-TR-94-002, Institute of Parallel Computing, School of Engineering and Applied Science, University of Virginia.

  • Bellotto, N., & Hu, H. (2005). Multisensor integration for human-robot interaction. The IEEE Journal of Intelligent Cybernetic Systems, 1. Available at: http://www.cybernetic.org.uk/ics.

  • Bellotto, N., & Hu, H. (2006). Vision and laser data fusion for tracking people with a mobile robot. In Proceedings of IEEE international conference on robotics and biomimetics (ROBIO) (pp. 7–12). Kunming, China.

  • Bellotto, N., & Hu, H. (2009). Multisensor-based human detection and tracking for mobile service robots. IEEE Transactions on Systems, Man, and Cybernetics—Part B, 39(1), 167–181.

    Article  Google Scholar 

  • Beymer, D., & Konolige, K. (2001). Tracking people from a mobile platform. In IJCAI workshop on reasoning with uncertainty in robotics. Seattle, WA, USA.

  • Bobruk, J., & Austin, D. (2004). Laser motion detection and hypothesis tracking from a mobile platform. In Proceedings of the 2004 Australian conference on robotics & automation. Canberra, Australia.

  • Bradski, G., Kaehler, A., & Pisarevsky, V. (2005). Learning-based computer vision with Intel’s open source computer vision library. Intel Technology Journal, 09(02), 119–130.

    Google Scholar 

  • Burgard, W., Trahanias, P., Hähnel, D., Moors, M., Schulz, D., Baltzakis, H., & Argyros, A. (2002). TOURBOT and WebFAIR: web-operated mobile robots for tele-presence in populated exhibitions. In Proceedings of the IROS 2002 workshop on robots in exhibitions.

  • Chakravarty, P., & Jarvis, R. (2006). Panoramic vision and laser range finder fusion for multiple person tracking. In Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2949–2954). Beijing, China.

  • Colegrove, S., Cheung, B., & Davey, S. (2003). Tracking system performance assessment. In Proceedings of the 6th international conference on information fusion (pp. 926–933). Cairns, Australia.

  • Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo methods in practice. Berlin: Springer.

    MATH  Google Scholar 

  • Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings of Radar and Signal Processing, 140(2), 107–113.

    Article  Google Scholar 

  • Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of SPIE AeroSense symposium (pp. 182–193). FL, USA.

  • Julier, S. J., Uhlmann, J. K., & Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transaction on Automatic Control, 45(3), 477–482.

    Article  MATH  MathSciNet  Google Scholar 

  • Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transaction of the ASME—Journal of Basic Engineering, 82, 35–45.

    Google Scholar 

  • Kwok, C., Fox, D., & Meilă, M. (2004). Real-time particle filters. Proceeding of the IEEE, 92(3), 469–484.

    Article  Google Scholar 

  • Liu, J. N. K., Wang, M., & Feng, B. (2005). iBotGuard: an internet-based intelligent robot security system using invariant face recognition against intruder. IEEE Transaction on Systems, Man, and Cybernetics (Part C), 35(1), 97–105.

    Article  Google Scholar 

  • Merwe, R. V. D., Doucet, A., Freitas, N. D., & Wan, E. (2000). The unscented particle filter. CUED/F-INFENG TR 380, Cambridge University Engineering Department.

  • Montemerlo, M., Whittaker, W., & Thrun, S. (2002). Conditional particle filters for simultaneous mobile robot localization and people-tracking. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 695–701). Washington, DC, USA.

  • Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman filter: particle filters for tracking applications. Norwood: Artech House.

    MATH  Google Scholar 

  • Schulz, D., Burgard, W., Fox, D., & Cremers, A. B. (2003a). People tracking with mobile robots using sample-based joint probabilistic data association filters. International Journal of Robotics Research, 22(2), 99–116.

    Article  Google Scholar 

  • Schulz, D., Fox, D., & Hightower, J. (2003b). People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters. In Proceedings of the international joint conference on artificial intelligence (IJCAI) (pp. 921–926). Acapulco, Mexico.

  • Tapus, A., Mataric, M. J., & Scasselati, B. (2007). Socially assistive robotics. IEEE Robotics and Automation Magazine, 14(1), 35–42.

    Article  Google Scholar 

  • Treptow, A., Cielniak, G., & Duckett, T. (2005). Active people recognition using thermal and grey images on a mobile security robot. In Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2103–2108). Canada.

  • Uhlmann, J. K. (2001). Introductions to the algorithmics of data association in multiple-target tracking. In Hall, D. L., & Llinas, J. (Eds.) Handbook of multisensor data fusion. Boca Raton: CRC Press.

    Google Scholar 

  • Viola, P., & Jones, M. J. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE conference on computer vision and pattern recognition (pp. 511–518). Kauai, HI, USA.

  • Vitruvius (1914). Ten books on architecture. Project Gutenberg. English translation by M. H. Morgan.

  • Welch, G., & Bishop, G. (2004). An introduction to the Kalman filter. Technical report 95-041, University of North Carolina.

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK

    Nicola Bellotto

  2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Huosheng Hu

Authors
  1. Nicola Bellotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huosheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicola Bellotto.

Electronic Supplementary Material

Human tracking in Office 1 with EKF, UKF and SIR particle filter. (MPG 11.2 MB)

Human tracking in the laboratory with EKF, UKF and SIR particle filter. (MPG 11 MB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bellotto, N., Hu, H. Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters. Auton Robot 28, 425–438 (2010). https://doi.org/10.1007/s10514-009-9167-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-009-9167-2

Search

Navigation