Abstract
A method to register dynamic and semi-static objects with an a-priori known static map is proposed. Candidates for semi-static objects are extracted from laser data based on a-priori information (shape and size) of common indoor objects. Expectation Maximization serves at the same time scan alignment and data association between scan data andthe static a-priori map and the semi-static map. Dynamic objects are identified as outliers in data association. Evidence of visible but unmatched parts is gathered using recursive Bayesian updates to yield reliable candidate rejection in the presence of sensor noise. This allows the resulting semi-static map to adapt to changes in the environment, which is demonstrated experimentally.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
D. Avots, E. Lim, R. Thibaux, and S. Thrun. A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments. In Proceedings of the Conference on Intelligent Robots and Systems (IROS), Lausanne, Switzerland, 2002.
J. Bilmes. A gentie tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models, 1997.
M. Davis, G. Efstathiou, C. S. Frenk, and S. D. M. White. The evolution of large-scale structure in a universe dominated by cold dark matter. Astrophysical Journal, 292:371–394, 1985.
A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 1(39):1–38, 1977.
Daniei J. Eisenstein and Piet Hut. Hop: A new group finding algorithm for n-body simulation. The Astrophysical Journal, pages 137–142, 1998.
B. Jensen, R. Philippsen, and R. Siegwart. Motion detection and path planning in dynamic environments. In Workshop Reasoning with Uncertainty in Robotics, Acapulco, Mexico, August 2003.
B. Jensen and R. Siegwart. Using EM to detect motion with mobile platforms. In IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, USA, 2003.
B. Kluge, C. Köhler, and E. Prassler. Fast and robust tracking of multiple moving objects with a laser range finder. In Proc. of Int. Conf. on Robotics and Automation, pages 1683–1688, 2001.
Arthur R. Pope. Model-based object recognition-A survey of recent research, 1994.
D. Schulz and W. Burgard. Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems, 34(2–3), 2001.
D. Schulz, W. Burgard, D. Fox, and A. Cremers. Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In Proceedings of the 2001 IEEE International Conference on Robotics and Automation, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, B., Ramel, G., Siegwart, R. (2003). Detecting Semi-static Objects with a Laser Scanner. In: Dillmann, R., Wörn, H., Gockel, T. (eds) Autonome Mobile Systeme 2003. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18986-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-18986-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20142-7
Online ISBN: 978-3-642-18986-9
eBook Packages: Springer Book Archive