Mobile Robot Localization using Soft-reduced Hypotheses Tracking
Mobile robot localization is the problem of determining the pose (position and orientation) of a mobile robot under complex measurement uncertainties. The Soft-reduced Hypotheses Tracking algorithm introduced here is based on the modified multiple model and exploits a soft gating of the measurements to reduce the computational requirements of the approach. The position part is based on an x- and y-histograms scan matching procedure, where x- and y-histograms are extracted directly from local occupancy grid maps using probability scalar transformation. The orientation part is based on the proposed obstacle vector transformation combined with polar histograms. Proposed algorithms are tested using a Pioneer 2DX mobile robot.
KeywordsMobile Robot Sequential Monte Carlo Global Localization Occupancy Grid Mobile Robot Localization
Unable to display preview. Download preview PDF.
- D. Lee, W. Chung, M. Kim.: “Probabilistic Localization of the Service Robot by Map Matching Algorithm”, in Proc. Of International Conference on Control, Automation and Systems (ICCAS’2002), Korea, 2002, pp.1667-1627.Google Scholar
- S. Thrun, W. Burgard, D. Fox, “A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping”, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-2000), IEEE PRESS, pp. 321-328, 2000.Google Scholar
- R. Karlsson: “Simulation Based Methods for Target Tracking”, Ph.D. Dissertation, Department of Electrical Engineering, Linkoeping University, Sweden, 2002.Google Scholar
- M. Hadzagic, H. Michalska, A. Jouan. “IMM-JVC and IMM-JPDA for closely manoeuvring targets”, the 35$th$ Asilomar Conference on Signals, Systems and Computers, 2001, pp. 1278-1282Google Scholar
- Y. Bar-Shalom, X. R. Li, T. Kirubarajan: “Estimation with Applications to Tracking and Navigation”, A Wiley –Interscience Publication John Wiley& Sons Inc., 2001.Google Scholar
- T. Duckett, Concurrent map building and self-localization for mobile robot navigation, PhD Thesis, University of Manchester, 2000.Google Scholar
- L. Banjanović-Mehmedović, Autonomus Mobile Robots Localization in large indoor Environments by using Ultrasound Range Sensors, PhD Thesis, University in Zagreb, 2006.Google Scholar
- A. Elfes, “Using Occupancy Grids for Mobile Robot Perception and Navigation”, Proceedings of IEEE International Conference on Robotics and Automation, Vol. 2, pp. 727-733, 1988.Google Scholar
- R. Hinkel, T. Knieriemen, “Environment perception with a laser radar in a fast moving robot”, Proceed. of the Symposium on Robot Control (SYROCO’88), Germany, 1988.Google Scholar
- E. Ivanjko, I. Petrović, N. Perić, “An approach to odometry calibration of differential drive mobile robots”, Proceedings of International Conference on Electrical Drives and Power Electronics EDPE’03, September 24-26, 2003, High Tatras, Slovakia, pp. 519-523.Google Scholar