Skip to main content
SpringerLink
Log in

A Probabilistic Approach to Collaborative Multi-Robot Localization

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.

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

  • Arkin, R.C. and Balch, T. 1998. Cooperative multiagent robotic systems. In Artificial Intelligence and Mobile Robots, D. Kortenkamp, R.P. Bonasso, and R. Murphy (Eds.), MIT/AAAI Press: Cambridge, MA.

    Google Scholar 

  • Betke, M. and Gurvits, L. 1993. Mobile robot localization using landmarks. Technical Report SCR-94-TR-474, Siemens Corporate Research, Princeton. Will also appear in the IEEE Transactions on Robotics and Automation.

  • Borenstein, J. 1987. The nursing robot system. Ph.D. Thesis, Technion, Haifa, Israel.

  • Borenstein, J. 1995. Control and kinematic design of multi-degreeof-freedom robots with compliant linkage. IEEE Transactions on Robotics and Automation, 11:21–35.

    Google Scholar 

  • Borenstein, J., Everett, B., and Feng, L. 1996. Navigating Mobile Robots: Systems and Techniques, A.K. Peters, Ltd.: Wellesley, MA.

    Google Scholar 

  • Boyen, X. and Koller, D. 1999. Exploiting the architecture of dynamic systems. In Proc.of the National Conference on Artificial Intelligence (AAAI).

  • Burgard, W., Cremers, A.B., Fox, D., H¨ahnel, D., Lakemeyer, G., Schulz, D., Steiner, W., and Thrun, S. Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1/2), 2000.

  • Burgard, W., Derr, A., Fox, D., and Cremers, A.B. 1998. Integrating global position estimation and position tracking for mobile robots: The Dynamic Markov Localization approach. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Burgard, W., Fox, D., Hennig, D., and Schmidt, T. 1996. Estimating the absolute position of a mobile robot using position probability grids. In Proc.of the National Conference on Artificial Intelligence (AAAI).

  • Burgard, W., Fox, D., and Thrun, S. 1997. Active mobile robot localization. In Proc.of the International Joint Conference on Artificial Intelligence (IJCAI).

  • Cao, Y.U., Fukunaga, A.S., and Kahng, A.B. 1997. Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4:1–23.

    Google Scholar 

  • Carpenter, J., Clifford, P., and Fernhead, P. 1997. An improved particle filter for non-linear problems. Technical report, Department of Statistics, University of Oxford.

  • Collet, T.S. and Cartwright, B.A. 1985. Landmark learning in bees. Journal of Comparative Physiology.

  • Cox, I.J. 1991. Blanche—An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, 7(2):193–204.

    Google Scholar 

  • Cox, I.J. and Wilfong, G.T. (Eds.). 1990.Autonomous Robot Vehicles. Springer Verlag.

  • Dean, T.L. and Boddy,M. 1988. An analysis of time-dependent planning. In Proc.of the National Conference on Artificial Intelligence (AAAI), pp. 49–54.

  • Dellaert, F., Fox, D., Burgard, W., and Thrun, S. 1999. Monte Carlo localization for mobile robots. In Proc.of the IEEE International Conference on Robotics & Automation (ICRA).

  • Doucet, A. 1998. On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/F-INFENG/TR.310, Department of Engineering, University of Cambridge.

  • Engelson, S. 1994. Passivemaplearning and visual place recognition. Ph.D. Thesis, Department of Computer Science, Yale University.

  • Everett, H.R., Gage, D.W., Gilbreth, G.A., Laird, R.T., and Smurlo, R.P. 1994. Real-world issues in warehouse navigation. In Proc.of the SPIE Conference on Mobile Robots IX, Boston, MA, Vol. 2352.

  • Fox, D., Burgard, W., Dellaert, F., and Thrun, S. 1999a. Monte Carlo localization: Efficient position estimation for mobile robots. In Proc.of the National Conference on Artificial Intelligence (AAAI).

  • Fox, D., Burgard, W., and Thrun, S. 1998a. Active Markov localization for mobile robots. Robotics and Autonomous Systems, 25:195–207.

    Google Scholar 

  • Fox, D., Burgard, W., and Thrun, S. 1999b. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11.

  • Fox, D., Burgard, W., Thrun, S., and Cremers, A.B. 1998b. Position estimation for mobile robots in dynamic environments. In Proc.of the National Conference on Artificial Intelligence (AAAI).

  • Fukuda, T., Ito, S., Oota, N., Arai, F., Abe, Y., Tanake, K., and Tanaka, Y. 1993. Navigation system based on ceiling landmark recognition for autonomous mobile robot. In Proc.of the International Conference on Industrial Electronics Control and Instrumentation (IECON), Vol. 1, pp. 1466–1471.

    Google Scholar 

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

    Google Scholar 

  • Greiner, R. and Isukapalli, R. 1994. Learning to select useful landmarks. In Proc.of the National Conference on Artificial A Probabilistic Approach to Collaborative Multi-Robot Localization 343 Intelligence (AAAI), AAAI Press/The MIT Press: Menlo Park, CA, pp. 1251–1256.

  • Gutmann, J.-S. and Schlegel, C. 1996. AMOS: Comparison of scan matching approaches for self-localization in indoor environments. In Proc.of the 1st Euromicro Workshop on Advanced Mobile Robotss, IEEE Computer Society Press.

  • Gutmann, J.-S., Weigel, T., and Nebel, B. 1999. Fast, accurate, and robust self-localization in polygonal environments. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Hinkel, R. and Knieriemen, T. 1988. Environment perception with a laser radar in a fast moving robot. In Proc.of Symposium on Robot Control, Karlsruhe, Germany, pp. 68.1–68.7.

  • Horswill, I. 1994. Specialization of perceptual processes. Technical Report AI TR-1511, MIT, AI Lab, Cambridge, MA.

  • Isard, M. and Blake, A. 1996. Contour tracking by stochastic propagation of conditional density. In Proc.of European Conference on Computer Vision (ECCV), pp. 343–356.

  • Isard, M. and Blake, A. 1998. Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28.

    Google Scholar 

  • Kaelbling, L.P., Cassandra, A.R., and Kurien, J.A. 1996. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Kanazawa, K., Koller, D., and Russell, S.J. 1995. Stochastic simulation algorithms for dynamic probabilistic networks. In Proc.of the 11th Annual Conference on Uncertainty in AI (UAI), Montreal, Canada.

  • rKing, S. and Weiman, C. 1990. Helpmate autonomous mobile robot navigation system. In Proc.of the SPIE Conference on Mobile Robots, Vol. 2352, Boston, MA, pp. 190–198.

    Google Scholar 

  • Kitagawa, G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1).

  • Koller, D. and Fratkina, R. 1998. Using learning for approximation in stochastic processes. In Proc.of the International Conference on Machine Learning (ICML).

  • Konolige, K. 1999. Markov localization using correlation. In Proc.of the International Joint Conference on Artificial Intelligence (IJCAI).

  • Kortenkamp, D., Bonasso, R.P., and Murphy, R. (Eds.). 1998. AIBased Mobile Robots: Case Studies of Successful Robot Systems, MIT Press: Cambridge, MA.

    Google Scholar 

  • Kortenkamp, D. and Weymouth, T. 1994. Topological mapping for mobile robots using a combination of sonar and vision sensing. In Proc.of the National Conference on Artificial Intelligence (AAAI).

  • Kruppa, H. 1999. Relative multi-robot localization: A probabilistic approach. Master's Thesis, ETH Z¨urich.

  • Kurazume, R. and Shigemi, N. 1994. Cooperative positioning with multiple robots. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Leonard, J.J. and Durrant-Whyte, H.F. 1992. Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic Publishers: Boston, MA.

    Google Scholar 

  • Leonard, J.J., Durrant-Whyte, H.F., and Cox, I.J. 1992. Dynamic map building for an autonomous mobile robot. International Journal of Robotics Research, 11(4):89–96.

    Google Scholar 

  • Lu, F. and Milios, E. 1997. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4:333–349.

    Google Scholar 

  • Matari´c, M.J. 1990. A distributed model for mobile robot environment-learning and navigation. Master's Thesis, MIT, Cambridge, MA: Also available as MIT AI Lab Tech Report AITR-1228.

  • Maybeck, P.S. 1990. The Kalman filter: An introduction to concepts. In Cox and Wilfong (1990).

  • Moore, A.W., Schneider, J., and Deng, K. 1997. Efficient locally weighted polynomial regression predictions. In Proc.of the International Conference on Machine Learning (ICML), Morgan Kaufmann Publishers.

  • Neven, H. and Sch¨oner, G. 1996. Dynamics parametrically controlled by image correlations organize robot navigation. Biological Cybernetics, 75:293–307.

    Google Scholar 

  • Nourbakhsh, I., Powers, R., and Birchfield, S. 1995. DERVISH an office-navigating robot. AI Magazine, 16(2).

  • Omohundro, S.M. 1987. Efficient algorithms with neural network behavior. Journal of Complex Systems, 1(2).

  • Omohundro, S.M. 1991. Bumptrees for efficient function, constraint, and classification learning. In Advances in Neural Information Processing Systems 3, R.P. Lippmann, J.E. Moody, and D.S. Touretzky (Eds.), Morgan Kaufmann.

  • Oore, S., Hinton, dG.E., and Dudek, G. 1997. A mobile robot that learns its place. Neural Computation, 9.

  • Peters, L., Surmann, H., Guo, S., Beck, K., and Huser, J. 1994. MORIA—Fuzzy Logik gesteuertes, autonomes Fahrzeug. In German.

  • Rekleitis, I.M., Dudek, G., and Milios, E. 1997. Multi-robot exploration of an unknown environment, efficiently reducing the odometry error. In Proc.of the International Joint Conference on Artificial Intelligence (IJCAI).

  • Rencken, W.D. 1993. Concurrent localisation and map building for mobile robots using ultrasonic sensors. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Rubin, D.B. 1988. Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3. M.H. Bernardo, K.M. an De-Groot, D.V. Lindley, and A.F.M. Smith (Eds.), Oxford University Press: Oxford, UK.

    Google Scholar 

  • Schiele, B. and Crowley, J.L. 1994. A comparison of position estimation techniques using occupancy grids. In Proc.of the IEEE International Conference on Robotics & Automation (ICRA).

  • Schulz, D., Burgard, W., and Cremers, A.B. 1999. Robust visualization of navigation experiments with mobile robots over the internet. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Shatkey, H. and Kaelbling, L.P. 1997. Learning topological maps with weak local odometric informations. In Proc.of the International Joint Conference on Artificial Intelligence (IJCAI).

  • Simmons, R. and Koenig, S. 1995. Probabilistic robot navigation in partially observable environments. In Proc.of the International Joint Conference on Artificial Intelligence (IJCAI).

  • Smith, R., Self, M., and Cheeseman, P. 1990Estimating uncertain spatial relationships in robotics. In Autonomous Robot Vehicles, I. Cox and G. Wilfong (Eds.), Springer Verlag, pp. 167–193.

  • Tanner, M.A. 1993. Tools for Statistical Inference, 2nd (Ed.), Springer Verlag: New York.

    Google Scholar 

  • Thrun, S. 1998a. Bayesian landmark learning for mobile robot localization. Machine Learning, 33(1):41–76.

    Google Scholar 

  • Thrun, S. 1998b. Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1):27–71.

    Google Scholar 

  • Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B. Dellaert, F., Fox, D., H¨ahnel, D., Rosenberg, C., Schulte, J., and Schulz, D. 1999a. MINERVA: A second-generation museum tour-guide robot. In Proc.of the International Conference on Robotics and Automation (ICRA' 99).

  • Thrun, S., Fox, D., and Burgard, W. 1998. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning, 31:29–53. Also appeard in Autonomous Robots 5, pp. 253–271, joint issue.

    Google Scholar 

  • Thrun, S., Langford, J., and Fox, D. 1999b. Monte Carlo hidden Markov models: Learning non-parametric models of partially observable stochastic processes. In Proc.of the International Conference on Machine Learning (ICML).

  • Weiß, G., Wetzler, C., and von Puttkamer, E. 1994. Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. In Proc.of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

  • Wolfart, E., Fisher, R.B., and Walker, A. 1995. Position refinement for a navigating robot using motion information based on honey bee strategies. In Proc.of the International Symposium on Robotic Systems (SIR 95), Pisa, Italy.

  • Zilberstein, S. and Russell, S. 1995. Approximate reasoning using anytime algorithms. In Imprecise and Approximate Computation, S. Natarajan (Ed.), Kluwer Academic Publishers: Dordrecht.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Dieter Fox

  2. Department of Computer Science, University of Freiburg, D-79110, Freiburg, Germany

    Wolfram Burgard

  3. Department of Computer Science, ETH Zürich, CH-8092, Zürich, Switzerland

    Hannes Kruppa

  4. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Sebastian Thrun

Authors
  1. Dieter Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfram Burgard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hannes Kruppa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sebastian Thrun
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fox, D., Burgard, W., Kruppa, H. et al. A Probabilistic Approach to Collaborative Multi-Robot Localization. Autonomous Robots 8, 325–344 (2000). https://doi.org/10.1023/A:1008937911390

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008937911390

Search

Navigation