2006 S1 Group 5
From Experimental Robotics
Contents |
Members
- Zhenyu Yang
- Hamid Teimoori
Project
Localization and map building are essentially two separate tasks. The robot localization identifies the position while mapping uses the on-board sensors to map out its environment. Nevertheless, localization and mapping are closely related. In this project, the goal is to integrate a SLAM method into robot system, which can be implemented with more than one robot, i.e. cooperative SLAM, in order to get a global map of environment. In this regard, using HIMM method, we designed an algorithm, which can be applied on-line or off-line for mapping. Due to the fact that in each scanning time some information is added to the map, this method can be applied in cooperative mapping in order to generate a map of a large environment. Implementation has been carried out on simulation software (Player/Stage) and also on pioneer robot. The results in mapping algorithm are quite satisfactory but there are some problems in localization due to odometry errors which have passive effect on mapping part. Future work can be done to modify the algorithm so the true robot localization can be achieved.
Theories
Map Building
Algorithm Implementation
Further Work
In this project, we have gained miscellaneous ways of implementing SLAM, and given our own Occupancy Grid algorithm. After practically testing these mechanisms, we have achieved our project aim which is to build up a cooperative SLAM algorithm. The experiments proved that the algorithm we were using has great performance, comparing with other existing algorithms.
Further tasks could be done in many ways. One possibility is combining a localization method for robot, such as AMCL. And HIMM could be involved in the more complex mechanism. Moreover, other SLAM methods, e.g. EKF, Particle Filtering, etc, may replace the HIMM algorithm.
References and More Details
- G. Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, and M. Csorba, “A Solution to the Simultaneous Localization and Map Building (SLAM) Problem,” IEEE Trans. of R & A, 2001
- Austin I. Eliazar and Ronald Parr. “DP-Slam2” Department of Computer Science Duke University
- M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” AAAI, 2002
- Borenstein, H. Y Koren, Y., “Histogramic in Motion Mapping for Mobile Robot Obstacle Avoidance”. IEEE Transactions on Robotics and Automation. Volumen 7. Nber 4. 1991
- Ph.D. thesis, Tim Bailey, ”Mobile Robot Localization and Mapping in Extensive Outdoor Environments” , Australian Centre for Field Robotics Department of Aerospace, Mechanical and Mechatronic Engineering The University of Sydney ,August 2002
- Fox D., Hightower J., Liao L. and Schulz D., “Bayesian Filtering for Location Estimation”, IEEE Pervasive Computing, pp. 10-19, July-September 2003
- Gutman J., “An experimental comparison of localisation methods continued”, in International Conference on Intelligent Robots and Systems, October 2002
- Moravec, H. and A. E. Elfes, “High resolution maps from wide angle sonar.” Proceedings of the 1985 IEEE International Conference on Robotics and Automation, pp. 116–121 , 1985, March
- Moravec, H. P. and Elfes, A., "High Resolution Maps from Wide Angle Sonar." Proceedings of the IEEE Conference on Robotics and Automation , Washington, D.C., 1985, pp. 116-121
- J. Borenstein and Y. Koren, “HISTOGRAMIC IN-MOTION MAPPING FOR MOBILE ROBOT OBSTACLE AVOIDANCE” , IEEE Journal of Robotics and Automation, Vol. 7, No. 4, 1991, pp. 535-539. 1991
- G. SICK AG, Industrial Safety Systems, “Sick: Proximity laser scanner.” Technical Description, 05 2002
- Wikipedia, the free encyclopedia
- "Player", June 5, 2006
- "Stage", June 5, 2006
- ActivMedia Robotics
- Player/Stage Website
