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CAD-MAP and estimation of ALV positions in mountainous areas

Published online by Cambridge University Press:  09 March 2009

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Summary

Position estimation is a key issue for an ALV Autonomous Land Vehicle) in navigating a mountainous area. The unevenness of the terrain makes mechanical velocity sensors inaccurate (due to wheel slippage), and the lack of appropriate landmarks complicates the problem. In this paper, we present a solution method using features of the skyline. The skyline from the vision system is assumed given, and compared with a computer map, called the CAD-MAP. The algorithm is composed of: a) Identification of the peak points in the camera skyline, b) Computing the ALV position for the identified peak points, and c) Searching for the corresponding peak point in the CAD-MAP. Heuristics for computational efficiency and solution accuracy are also included in the algorithm. To test the validity and effectiveness of the algorithm, numerous simulations were performed and analyzed.

Keywords

Autonomous land vehicleMobile robot navigationComputer mapPosition estimationPath planning
Type
Article
Information
Robotica , Volume 12 , Issue 4 , July 1994 , pp. 287 - 297
Copyright
Copyright © Cambridge University Press 1994

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References

1.Shiller, Z. and Chen, J., “Optimal motion planning of autonomous vehicles in three dimensional terrains” Proc. IEEE Int. Conf. Robot. Automat. (May 1990) pp. 198–203.Google Scholar
2.Ernsberge, R. and Zeman, N., “New Products: A Satellite Fix” Business Week, USA, 3 (02. 17, 1992).Google Scholar
3.Fukui, I., “TV image processing to determine the position of a robot vehicle” Pattern Recognition 14, Nos. 1–6, 11–19 (1981).CrossRefGoogle Scholar
4.Kabuka, M. and Arenas, A., “Position verification of a mobile robot using a standard pattern” IEEE J. of Robot, and Automat. RA-3, No. 6, 505–516 (Dec. 1987).CrossRefGoogle Scholar
5.Krotkov, E., “Mobile robot localization using a single image” Proc. IEEE Int. Conf. Robot, and Automat. 978–983 (May 1989).Google Scholar
6.Kumar, R., “Determination of the camera location and orientation” Proc. DARPA Image Understanding Workshop (April 1988) pp. 870–881.Google Scholar
7.Levitt, T., Lawton, D., Chelberg, D. and Nelson, P., “Qualitative navigation” DARPA Image Understanding Workshop (Feb, 1987) pp. 447–465.Google Scholar
8.Nasr, H. and Bhanu, B., “Landmark recognition system for autonomous mobile robots” Proc. IEEE Conf. Robot, and Automat. (April 1988) pp. 1105–1111.Google Scholar
9.Suh, S., Jee, W. and Kang, J., “Development of a CAD-MAP system for 3D topography” Proc. KIIE Fall Meeting, Seoul, Korea (Oct. 1991), pp. 159–174.Google Scholar
10.Talluri, R. and Aggarwal, J., “Position Estimation for a Mobile Robot in an Unstructured Environment” Proc. IEEE Int. Conf. IROS 90 (June 1990) pp. 159–166.Google Scholar
11.Park, C., Interactive Microcomputer Graphics (Addison- Wesley, Reading MA, 1985).Google Scholar