Guaranteed Mobile Robot Tracking Using Robust Interval Constraint Propagation
The paper presents an approach for localizing a mobile robot in a feature-based map using a 2D laser rangefinder and wheel odometry. As the presented approach is based on set membership methods, the localization result consists of sets instead of points, and is guaranteed to contain the true robot position as long as the sensor errors are absolutely bounded and a maximum number of measurement outliers can be assumed. It is able to cope with a multitude of measurement per time step compared to previous approaches. Moreover, the approach is capable of identifying and marking outlier points in the laser range scan. A real world experiment, where a mobile robot is moving in a structured indoor environment with previously unmapped static and dynamic obstacles shows the feasibility of the approach. It is shown that the true robot pose is always included in the solution set, which is computed in real time.
KeywordsMobile robot localization tracking set membership constraint propagation outlier detection
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- 1.Hanebeck, U., Schmidt, G.: Set theoretic localization of fast mobile robots using an angle measurement technique. In: Proc. IEEE International Conference on Robotics and Automation, vol. 2, pp. 1387–1394 (1996)Google Scholar
- 2.Sabater, A., Thomas, F.: Set membership approach to the propagation of uncertain geometric information. In: Proc. IEEE International Conference on Robotics and Automation, vol. 3, pp. 2718–2723 (1991)Google Scholar
- 3.Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Intelligent robotics and autonomous agents. MIT Press (September 2005)Google Scholar
- 5.Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: Proc. IEEE International Conference on Robotics and Automation, vol. 2, pp. 1322–1328 (1999)Google Scholar
- 7.Lambert, A., Gruyer, D., Vincke, B., Seignez, E.: Consistent outdoor vehicle localization by bounded-error state estimation. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1211–1216 (2009)Google Scholar
- 12.Waltz, D.: Understanding line drawings of scenes with shadows. In: The Psychology of Computer Vision, pp. 19–91. McGraw-Hill (1975)Google Scholar
- 13.Benhamou, F., Goualard, F., Granvilliers, L., Puget, J.F.: Revising hull and box consistency. In: Proc. International Conference on Logic programming, pp. 230–244. Massachusetts Institute of Technology, Cambridge (1999)Google Scholar
- 15.Seignez, E., Lambert, A.: Complexity study of guaranteed state estimation for real time to robot localization. Journal of Automation, Mobile Robotics & Intelligent Systems 5(2), 12–27 (2011)Google Scholar