Abstract
Localization is one of the fundamental problems in mobile robotics. Without knowledge about their position mobile robots cannot efficiently carry out their tasks. In this paper we present Markov localization as a technique for estimating the position of a mobile robot. The key idea of this technique is to maintain a probability density over the whole state space of the robot within its environment. This way our technique is able to globally localize the robot from scratch and even to recover from localization failures, a property which is essential for truly autonomous robots. The probabilistic framework makes this approach robust against approximate models of the environment as well as noisy sensors. Based on a fine-grained, metric discretization of the state space, Markov localization is able to incorporate raw sensor readings and does not require predefined landmarks. It also includes a filtering technique which allows to reliably estimate the position of a mobile robot even in densely populated environments. We furthermore describe, how the explicit representation of the density can be exploited in a reactive collision avoidance system to increase the robustness and reliability of the robot even in situations in which it is uncertain about its position. The method described here has been implemented and tested in several real-world applications of mobile robots including the deployments of two mobile robots as interactive museum tour-guides.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arras, K.O., Vestli, S.J.: Hybrid, high-precision localization for the mail distributing mobile robot system MOPS. In: Proc. of the IEEE International Conference on Robotics and Automation (1998)
Borenstein, J., Everett, B., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd., Wellesley (1996)
Buhmann, J., Burgard, W., Cremers, A.B., Fox, D., Hofmann, T., Schneider, F., Strikos, J., Thrun, S.: The mobile robot Rhino. AI Magazine 16(2) (1995)
Burgard, W., Cremers, A.B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S.: The interactive museum tour-guide robot. In: Proc.of the Fifteenth National Conference on Artificial Intelligence (1998)
Burgard, W., Derr, A., Fox, D., Cremers, A.B.: 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 (1998)
Burgard, W., Fox, D., Hennig, D.: Fast grid-based position tracking for mobile robots. In: Proc. of the 21st German Conference on Artificial Intelligence. Springer, Heidelberg (1997)
Burgard, W., Fox, D., Hennig, D., Schmidt, T.: Estimating the absolute position of a mobile robot using position probability grids. In: Proc. of the Thirteenth National Conference on Artificial Intelligence (1996)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)
Cox, I.J.: Blanche – an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation 7(2–4), 193–204 (1991)
Cox, I.J., Wilfong, G.T. (eds.): Autonomous Robot Vehicles. Springer, Heidelberg (1990)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. In: Proceedings of the International Conference on Robotics and Automation, ICRA 1999 (1999) (to appear)
Fox, D.: Markov Localization: A Probabilistic Framework for Mobile Robot Localization and Navigation. PhD thesis, Dept. of Computer Science, Univ. of Bonn, Germany (1998)
Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte Carlo localization—efficient position estimation for mobile robots. Proc.of the Sixteenth National Conference on Artificial Intelligence (1999) (to appear)
Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robotics and Automation 4(1) (1997)
Fox, D., Burgard, W., Thrun, S., Cremers, A.B.: Position estimation for mobile robots in dynamic environments. In: Proc.of the Fifteenth National Conference on Artificial Intelligence (1998)
Gutmann, J.-S., Burgard, W., Fox, D., Konolige, K.: An experimental comparison of localization methods. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1998)
Gutmann, J.-S., Schlegel, C.: AMOS: Comparison of scan matching approaches for self-localization in indoor environments. In: Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, Los Alamitos (1996)
Hennig, D.: Globale und lokale Positionierung mobiler Roboter mittels Wahrscheinlichkeitsgittern. Master’s thesis, Department of Computer Science, University of Bonn, Germany (1997) (In German)
Hertzberg, J., Kirchner, F.: Landmark-based autonomous navigation in sewerage pipes. In: Proc. of the First Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, Los Alamitos (1996)
Kaelbling, L.P., Cassandra, A.R., Kurien, J.A.: Acting under uncertainty: Discrete bayesian models for mobile-robot navigation. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1996)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Tansaction of the ASME – Journal of basic engineering, 35–45 (1960)
Kortenkamp, D., Bonasso, R.P., Murphy, R. (eds.): Artificial Intelligence and Mobile Robots. MIT/AAAI Press, Cambridge (1998)
Leonard, J.J., Durrant-Whyte, H.F.: Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7(3), 376–382 (1991)
Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2d range scans. In: IEEE Computer Vision and Pattern Recognition Conference, CVPR (1994)
Maybeck, P.S.: Autonomous robot vehicles. Cox and Wilfong
Moravec, H.P.: Sensor fusion in certainty grids for mobile robots. AI Magazine (Summer 1988)
Nourbakhsh, I., Powers, R., Birchfield, S.: DERVISH an office-navigating robot. AI Magazine 16(2) (Summer 1995)
Schiele, B., Crowley, J.L.: A comparison of position estimation techniques using occupancy grids. In: Proc. of the IEEE International Conference on Robotics and Automation (1994)
Shaffer, G., Gonzalez, J., Stentz, A.: Comparison of two range-based estimators for a mobile robot. In: SPIE Conf. on Mobile Robots VII, pp. 661–667 (1992)
Simmons, R., Koenig, S.: Probabilistic robot navigation in partially observable environments. In: Proc. of the International Joint Conference on Artificial Intelligence (1995)
Thrun, S.: Bayesian landmark learning for mobile robot localization. Machine Learning 33(1) (1998)
Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Schulte, J., Schulz, D.: MINERVA: A secondgeneration museum tour-guide robot. In: Proceedings of the International Conference on Robotics and Automation, ICRA 1999 (1999) (to appear)
Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig, D., Hofmann, T., Krell, M., Schimdt, T.: Map learning and high-speed navigation in RHINO. In: Kortenkamp, D., et al
Weiß, G., Wetzler, C., von Puttkamer, E.: 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 (1994)
Yamauchi, B.: Mobile robot localization in dynamic environments using dead reckoning and evidence grids. In: Proc. of the 1996 IEEE International Conference on Robotics and Automation (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fox, D., Burgard, W., Thrun, S. (1999). Markov Localization for Reliable Robot Navigation and People Detection. In: Christensen, H.I., Bunke, H., Noltemeier, H. (eds) Sensor Based Intelligent Robots. Lecture Notes in Computer Science(), vol 1724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10705474_1
Download citation
DOI: https://doi.org/10.1007/10705474_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66933-3
Online ISBN: 978-3-540-46619-2
eBook Packages: Springer Book Archive