Abstract
The ability to detect traversable terrains is essential for autonomous mobile robots to guarantee safe navigation. In this paper, we present a method for terrain classification for wheeled mobile robots. Our scope is limited to mobile service robots that are used for surveillance or delivery in semi-structured urban environments. A reliable terrain detection scheme is required for both indoor and outdoor applications anytime. A low-cost Lidar (Light detection and ranging) is adopted for terrain detection. To deal with intrinsic measurement errors and uncertainties of the Lidar, the classification criteria are trained through a supervised learning approach. Training data are obtained from manual driving at target environments. Various decision boundaries resulted from a variety of floor conditions, sensor types and robot platforms. The proposed terrain classification scheme is experimentally tested in success.
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Lee, H., Chung, W. Terrain Classification for Mobile Robots on the Basis of Support Vector Data Description. Int. J. Precis. Eng. Manuf. 19, 1305–1315 (2018). https://doi.org/10.1007/s12541-018-0154-4
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DOI: https://doi.org/10.1007/s12541-018-0154-4