Summary
In this paper we consider the problem of mobile robot localization with range sensors in outdoor environments. Our approach applies a particle filter to estimate the full six-dimensional state of the robot. To represent the environment we utilize multi-level surface maps which allow the robot to represent vertical structures and multiple levels in the environment. We describe probabilistic motion and sensor models to calculate the proposal distribution and to evaluate the likelihood of observations. Experimental results obtained with a mobile robot in an outdoor environment indicate that our approach can be used to robustly and accurately localize an outdoor vehicle. The experiments also demonstrate that multi-level surface maps lead to a significantly better localization performance than standard elevation maps.
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
Adams, M., Zhang, S., Xie, L.: Particle filter based outdoor robot localization using natural features extracted from laser scanners. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) (2004)
Agrawal, M., Konolige, K.: Real-time localization in outdoor environments using stereo vision and inexpensive gps. In: International Conference on Pattern Recognition (ICPR) (2006)
Bares, J., Hebert, M., Kanade, T., Krotkov, E., Mitchell, T., Simmons, R., Whittaker, W.R.L.: Ambler: An autonomous rover for planetary exploration. IEEE Computer Society Press 22(6), 18–22 (1989)
Davison, A., Kita, N.: 3d simultaneous localisation and map-building using active vision for a robot moving on undulating terrain. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Kauai, December 2001, IEEE Computer Society Press, Los Alamitos (2001)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) (1999)
Doucet, A., de Freitas, N., Gordan, N. (eds.): Sequential Monte-Carlo Methods in Practice. Springer, Heidelberg (2001)
Eliazar, A., Parr, R.: Learning probabilistic motion models for mobile robots. In: Proc. of the International Conference on Machine Learning (ICML) (2004)
Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Barcelona, Spain, pp. 2443–2448 (2005)
Hebert, M., Caillas, C., Krotkov, E., Kweon, I.S., Kanade, T.: Terrain mapping for a roving planetary explorer. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 997–1002 (1989)
Lacroix, S., Mallet, A., Bonnafous, D., Bauzil, G., Fleury, S., Herrb, M., Chatila, R.: Autonomous rover navigation on unknown terrains: Functions and integration. International Journal of Robotics Research 21(10-11), 917–942 (2002)
Lingemann, K., Surmann, H., Nüchter, A., Hertzberg, J.: High-speed laser localization for mobile robots. Journal of Robotics & Autonomous Systems 51(4), 275–296 (2005)
Olson, C.F.: Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation 16(1), 55–66 (2000)
Parra, C., Murrieta-Cid, R., Devy, M., Briot, M.: 3-d modelling and robot localization from visual and range data in natural scenes. In: Christensen, H.I. (ed.) ICVS 1999. LNCS, vol. 1542, pp. 450–468. Springer, Heidelberg (1998)
Pfaff, P., Triebel, R., Burgard, W.: An efficient extension to elevation maps for outdoor terrain mapping and loop closing. International Journal of Robotics Research (2007)
Thrun, S.: A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research 20(5), 335–363 (2001)
Triebel, R., Pfaff, P., Burgard, W.: Multi level surface maps for outdoor terrain mapping and loop closing. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kümmerle, R., Triebel, R., Pfaff, P., Burgard, W. (2008). Monte Carlo Localization in Outdoor Terrains Using Multi-Level Surface Maps. In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-75404-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75403-9
Online ISBN: 978-3-540-75404-6
eBook Packages: EngineeringEngineering (R0)