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Proceedings Paper

Terrain classification for a UGV
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Paper Abstract

This work addresses the issue of Terrain Classification that can be applied for path planning for an Unmanned Ground Vehicle (UGV) platform. We are interested in classification of features such as rocks, bushes, trees and dirt roads. Currently, the data is acquired from a color camera mounted on the UGV as we can add range data from a second sensor in the future. The classification is accomplished by first, coarse segmenting a frame and then refining the initial segmentations through a convenient user interface. After the first frame, temporal information is exploited to improve the quality of the image segmentation and help classification adapt to changes due to ambient lighting, shadows, and scene changes as the platform moves. The Mean Shift Classifier algorithm provides segmentation of the current frame data. We have tested the above algorithms with four sequence of frames acquired in an environment with terrain representative of the type we expect to see in the field. A comparison of the results from this algorithm was done with accurate manually-segmented (ground-truth) data, for each frame in the sequence.

Paper Details

Date Published: 27 May 2005
PDF: 8 pages
Proc. SPIE 5804, Unmanned Ground Vehicle Technology VII, (27 May 2005); doi: 10.1117/12.603970
Show Author Affiliations
Alok Sarwal, PercepTek Inc. (United States)
Chris Baker, PercepTek Inc. (United States)
Mark Rosenblum, PercepTek Inc. (United States)


Published in SPIE Proceedings Vol. 5804:
Unmanned Ground Vehicle Technology VII
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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